Numeric—function to automatically build 23 individual models and 17 ensembles then return the results to the user
Numeric.RdNumeric—function to automatically build 23 individual models and 17 ensembles then return the results to the user
Arguments
- data
data can be a CSV file or within an R package, such as MASS::Boston
- colnum
a column number in your data
- numresamples
the number of resamples
- how_to_handle_strings
0: No strings, 1: Factor values, 2: One-hot encoding, 3: One-hot encoding AND jitter
- do_you_have_new_data
"Y" or "N". If "Y", then you will be asked for the new data
- save_all_trained_models
"Y" or "N". If "Y", then places all the trained models in the Environment
- remove_ensemble_correlations_greater_than
Enter a number to remove correlations in the ensembles
- use_parallel
"Y" or "N" for parallel processing
- train_amount
set the amount for the training data
- test_amount
set the amount for the testing data
- validation_amount
Set the amount for the validation data
Examples
# Note that examples take about one minute each to run to completion
Numeric(data = Boston_housing,
colnum = 9,
numresamples = 2,
how_to_handle_strings = 0,
do_you_have_new_data = "N",
save_all_trained_models = "N",
remove_ensemble_correlations_greater_than = 1.00,
train_amount = 0.60,
test_amount = 0.20,
validation_amount = 0.20)
#> [1]
#> [1] "Resampling number 1 of 2,"
#> [1]
#> Number of parameters (weights and biases) to estimate: 32
#> Nguyen-Widrow method
#> Scaling factor= 0.7016246
#> gamma= 29.0744 alpha= 4.1223 beta= 14908.55
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:8.672059 test-rmse:9.438856
#> [2] train-rmse:6.152745 test-rmse:6.757185
#> [3] train-rmse:4.391829 test-rmse:4.949025
#> [4] train-rmse:3.171701 test-rmse:3.772709
#> [5] train-rmse:2.337117 test-rmse:3.056945
#> [6] train-rmse:1.750802 test-rmse:2.629145
#> [7] train-rmse:1.357173 test-rmse:2.429477
#> [8] train-rmse:1.112720 test-rmse:2.326774
#> [9] train-rmse:0.937890 test-rmse:2.251303
#> [10] train-rmse:0.842939 test-rmse:2.234661
#> [11] train-rmse:0.758184 test-rmse:2.224523
#> [12] train-rmse:0.690180 test-rmse:2.221965
#> [13] train-rmse:0.641760 test-rmse:2.207268
#> [14] train-rmse:0.600522 test-rmse:2.199320
#> [15] train-rmse:0.564462 test-rmse:2.191745
#> [16] train-rmse:0.533005 test-rmse:2.183271
#> [17] train-rmse:0.514395 test-rmse:2.177992
#> [18] train-rmse:0.489369 test-rmse:2.174245
#> [19] train-rmse:0.454472 test-rmse:2.154759
#> [20] train-rmse:0.425754 test-rmse:2.154337
#> [21] train-rmse:0.413945 test-rmse:2.155661
#> [22] train-rmse:0.383929 test-rmse:2.154634
#> [23] train-rmse:0.370814 test-rmse:2.156923
#> [24] train-rmse:0.343605 test-rmse:2.137285
#> [25] train-rmse:0.319275 test-rmse:2.125506
#> [26] train-rmse:0.309275 test-rmse:2.129290
#> [27] train-rmse:0.293044 test-rmse:2.130203
#> [28] train-rmse:0.282592 test-rmse:2.132242
#> [29] train-rmse:0.275252 test-rmse:2.130917
#> [30] train-rmse:0.267014 test-rmse:2.131481
#> [31] train-rmse:0.255299 test-rmse:2.128396
#> [32] train-rmse:0.246303 test-rmse:2.129907
#> [33] train-rmse:0.240403 test-rmse:2.132613
#> [34] train-rmse:0.226540 test-rmse:2.126627
#> [35] train-rmse:0.216594 test-rmse:2.124497
#> [36] train-rmse:0.204977 test-rmse:2.123194
#> [37] train-rmse:0.195543 test-rmse:2.119193
#> [38] train-rmse:0.188133 test-rmse:2.117895
#> [39] train-rmse:0.182978 test-rmse:2.109969
#> [40] train-rmse:0.175473 test-rmse:2.114348
#> [41] train-rmse:0.170247 test-rmse:2.112939
#> [42] train-rmse:0.165662 test-rmse:2.119608
#> [43] train-rmse:0.156838 test-rmse:2.119445
#> [44] train-rmse:0.147199 test-rmse:2.116993
#> [45] train-rmse:0.143322 test-rmse:2.115801
#> [46] train-rmse:0.142190 test-rmse:2.115630
#> [47] train-rmse:0.136266 test-rmse:2.113574
#> [48] train-rmse:0.130227 test-rmse:2.112950
#> [49] train-rmse:0.126008 test-rmse:2.112991
#> [50] train-rmse:0.121772 test-rmse:2.112085
#> [51] train-rmse:0.120031 test-rmse:2.111994
#> [52] train-rmse:0.117313 test-rmse:2.111213
#> [53] train-rmse:0.114091 test-rmse:2.111491
#> [54] train-rmse:0.108971 test-rmse:2.107528
#> [55] train-rmse:0.106118 test-rmse:2.107551
#> [56] train-rmse:0.103322 test-rmse:2.107101
#> [57] train-rmse:0.101635 test-rmse:2.107626
#> [58] train-rmse:0.099125 test-rmse:2.108066
#> [59] train-rmse:0.096801 test-rmse:2.107140
#> [60] train-rmse:0.095504 test-rmse:2.106842
#> [61] train-rmse:0.091867 test-rmse:2.107061
#> [62] train-rmse:0.086652 test-rmse:2.105820
#> [63] train-rmse:0.085660 test-rmse:2.103970
#> [64] train-rmse:0.081174 test-rmse:2.104043
#> [65] train-rmse:0.080254 test-rmse:2.105653
#> [66] train-rmse:0.079056 test-rmse:2.106071
#> [67] train-rmse:0.076620 test-rmse:2.105946
#> [68] train-rmse:0.074326 test-rmse:2.106405
#> [69] train-rmse:0.071799 test-rmse:2.103912
#> [70] train-rmse:0.070297 test-rmse:2.103901
#> [1] train-rmse:8.672059 validation-rmse:9.248575
#> [2] train-rmse:6.152745 validation-rmse:6.724812
#> [3] train-rmse:4.391829 validation-rmse:4.987419
#> [4] train-rmse:3.171701 validation-rmse:3.825682
#> [5] train-rmse:2.337117 validation-rmse:3.086599
#> [6] train-rmse:1.750802 validation-rmse:2.625707
#> [7] train-rmse:1.357173 validation-rmse:2.341788
#> [8] train-rmse:1.112720 validation-rmse:2.182632
#> [9] train-rmse:0.937890 validation-rmse:2.086531
#> [10] train-rmse:0.842939 validation-rmse:2.029346
#> [11] train-rmse:0.758184 validation-rmse:2.005768
#> [12] train-rmse:0.690180 validation-rmse:1.982328
#> [13] train-rmse:0.641760 validation-rmse:1.967961
#> [14] train-rmse:0.600522 validation-rmse:1.952020
#> [15] train-rmse:0.564462 validation-rmse:1.932925
#> [16] train-rmse:0.533005 validation-rmse:1.925439
#> [17] train-rmse:0.514395 validation-rmse:1.919393
#> [18] train-rmse:0.489369 validation-rmse:1.910180
#> [19] train-rmse:0.454472 validation-rmse:1.908855
#> [20] train-rmse:0.425754 validation-rmse:1.909573
#> [21] train-rmse:0.413945 validation-rmse:1.906480
#> [22] train-rmse:0.383929 validation-rmse:1.904949
#> [23] train-rmse:0.370814 validation-rmse:1.900377
#> [24] train-rmse:0.343605 validation-rmse:1.895194
#> [25] train-rmse:0.319275 validation-rmse:1.889892
#> [26] train-rmse:0.309275 validation-rmse:1.888411
#> [27] train-rmse:0.293044 validation-rmse:1.887381
#> [28] train-rmse:0.282592 validation-rmse:1.884272
#> [29] train-rmse:0.275252 validation-rmse:1.883587
#> [30] train-rmse:0.267014 validation-rmse:1.883730
#> [31] train-rmse:0.255299 validation-rmse:1.883102
#> [32] train-rmse:0.246303 validation-rmse:1.884843
#> [33] train-rmse:0.240403 validation-rmse:1.883799
#> [34] train-rmse:0.226540 validation-rmse:1.880617
#> [35] train-rmse:0.216594 validation-rmse:1.880885
#> [36] train-rmse:0.204977 validation-rmse:1.881063
#> [37] train-rmse:0.195543 validation-rmse:1.879183
#> [38] train-rmse:0.188133 validation-rmse:1.879829
#> [39] train-rmse:0.182978 validation-rmse:1.879483
#> [40] train-rmse:0.175473 validation-rmse:1.875394
#> [41] train-rmse:0.170247 validation-rmse:1.874632
#> [42] train-rmse:0.165662 validation-rmse:1.874871
#> [43] train-rmse:0.156838 validation-rmse:1.873415
#> [44] train-rmse:0.147199 validation-rmse:1.874174
#> [45] train-rmse:0.143322 validation-rmse:1.873496
#> [46] train-rmse:0.142190 validation-rmse:1.873346
#> [47] train-rmse:0.136266 validation-rmse:1.872541
#> [48] train-rmse:0.130227 validation-rmse:1.871743
#> [49] train-rmse:0.126008 validation-rmse:1.871542
#> [50] train-rmse:0.121772 validation-rmse:1.871289
#> [51] train-rmse:0.120031 validation-rmse:1.871249
#> [52] train-rmse:0.117313 validation-rmse:1.871138
#> [53] train-rmse:0.114091 validation-rmse:1.870434
#> [54] train-rmse:0.108971 validation-rmse:1.870209
#> [55] train-rmse:0.106118 validation-rmse:1.869854
#> [56] train-rmse:0.103322 validation-rmse:1.869964
#> [57] train-rmse:0.101635 validation-rmse:1.869437
#> [58] train-rmse:0.099125 validation-rmse:1.869641
#> [59] train-rmse:0.096801 validation-rmse:1.869278
#> [60] train-rmse:0.095504 validation-rmse:1.869140
#> [61] train-rmse:0.091867 validation-rmse:1.869391
#> [62] train-rmse:0.086652 validation-rmse:1.868535
#> [63] train-rmse:0.085660 validation-rmse:1.869984
#> [64] train-rmse:0.081174 validation-rmse:1.869602
#> [65] train-rmse:0.080254 validation-rmse:1.869450
#> [66] train-rmse:0.079056 validation-rmse:1.869270
#> [67] train-rmse:0.076620 validation-rmse:1.868947
#> [68] train-rmse:0.074326 validation-rmse:1.867989
#> [69] train-rmse:0.071799 validation-rmse:1.867720
#> [70] train-rmse:0.070297 validation-rmse:1.867792
#> [1]
#> [1] "Working on the Ensembles section"
#> [1]
#> Number of parameters (weights and biases) to estimate: 54
#> Nguyen-Widrow method
#> Scaling factor= 0.704307
#> gamma= 21.6953 alpha= 7.0486 beta= 4606.234
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:9.375610 test-rmse:9.291363
#> [2] train-rmse:6.653688 test-rmse:6.604651
#> [3] train-rmse:4.722735 test-rmse:4.688754
#> [4] train-rmse:3.353117 test-rmse:3.330016
#> [5] train-rmse:2.381159 test-rmse:2.366978
#> [6] train-rmse:1.691877 test-rmse:1.684374
#> [7] train-rmse:1.203320 test-rmse:1.201802
#> [8] train-rmse:0.855098 test-rmse:0.855418
#> [9] train-rmse:0.608626 test-rmse:0.610630
#> [10] train-rmse:0.432853 test-rmse:0.435232
#> [11] train-rmse:0.308131 test-rmse:0.310708
#> [12] train-rmse:0.219510 test-rmse:0.221943
#> [13] train-rmse:0.156590 test-rmse:0.157822
#> [14] train-rmse:0.111618 test-rmse:0.113201
#> [15] train-rmse:0.079494 test-rmse:0.080796
#> [16] train-rmse:0.056700 test-rmse:0.057997
#> [17] train-rmse:0.040565 test-rmse:0.041713
#> [18] train-rmse:0.029106 test-rmse:0.030098
#> [19] train-rmse:0.020930 test-rmse:0.021865
#> [20] train-rmse:0.015203 test-rmse:0.016209
#> [21] train-rmse:0.011182 test-rmse:0.012476
#> [22] train-rmse:0.008241 test-rmse:0.009965
#> [23] train-rmse:0.006183 test-rmse:0.008299
#> [24] train-rmse:0.004717 test-rmse:0.007304
#> [25] train-rmse:0.003640 test-rmse:0.006665
#> [26] train-rmse:0.002958 test-rmse:0.006336
#> [27] train-rmse:0.002383 test-rmse:0.006013
#> [28] train-rmse:0.001956 test-rmse:0.006013
#> [29] train-rmse:0.001595 test-rmse:0.005879
#> [30] train-rmse:0.001364 test-rmse:0.005929
#> [31] train-rmse:0.001132 test-rmse:0.005850
#> [32] train-rmse:0.000954 test-rmse:0.005798
#> [33] train-rmse:0.000783 test-rmse:0.005847
#> [34] train-rmse:0.000678 test-rmse:0.005917
#> [35] train-rmse:0.000566 test-rmse:0.005858
#> [36] train-rmse:0.000476 test-rmse:0.005815
#> [37] train-rmse:0.000417 test-rmse:0.005858
#> [38] train-rmse:0.000374 test-rmse:0.005844
#> [39] train-rmse:0.000332 test-rmse:0.005851
#> [40] train-rmse:0.000303 test-rmse:0.005864
#> [41] train-rmse:0.000303 test-rmse:0.005864
#> [42] train-rmse:0.000303 test-rmse:0.005864
#> [43] train-rmse:0.000303 test-rmse:0.005864
#> [44] train-rmse:0.000303 test-rmse:0.005865
#> [45] train-rmse:0.000303 test-rmse:0.005865
#> [46] train-rmse:0.000303 test-rmse:0.005865
#> [47] train-rmse:0.000303 test-rmse:0.005865
#> [48] train-rmse:0.000303 test-rmse:0.005865
#> [49] train-rmse:0.000303 test-rmse:0.005865
#> [50] train-rmse:0.000303 test-rmse:0.005865
#> [51] train-rmse:0.000303 test-rmse:0.005865
#> [52] train-rmse:0.000303 test-rmse:0.005865
#> [53] train-rmse:0.000303 test-rmse:0.005865
#> [54] train-rmse:0.000303 test-rmse:0.005865
#> [55] train-rmse:0.000303 test-rmse:0.005865
#> [56] train-rmse:0.000303 test-rmse:0.005865
#> [57] train-rmse:0.000303 test-rmse:0.005865
#> [58] train-rmse:0.000303 test-rmse:0.005865
#> [59] train-rmse:0.000303 test-rmse:0.005865
#> [60] train-rmse:0.000303 test-rmse:0.005865
#> [61] train-rmse:0.000303 test-rmse:0.005865
#> [62] train-rmse:0.000303 test-rmse:0.005865
#> [63] train-rmse:0.000303 test-rmse:0.005865
#> [64] train-rmse:0.000303 test-rmse:0.005865
#> [65] train-rmse:0.000303 test-rmse:0.005865
#> [66] train-rmse:0.000303 test-rmse:0.005865
#> [67] train-rmse:0.000303 test-rmse:0.005865
#> [68] train-rmse:0.000303 test-rmse:0.005865
#> [69] train-rmse:0.000303 test-rmse:0.005865
#> [70] train-rmse:0.000303 test-rmse:0.005865
#> [1] train-rmse:9.375610 validation-rmse:9.386035
#> [2] train-rmse:6.653688 validation-rmse:6.973941
#> [3] train-rmse:4.722735 validation-rmse:5.326155
#> [4] train-rmse:3.353117 validation-rmse:4.237620
#> [5] train-rmse:2.381159 validation-rmse:3.533406
#> [6] train-rmse:1.691877 validation-rmse:3.105013
#> [7] train-rmse:1.203320 validation-rmse:2.844492
#> [8] train-rmse:0.855098 validation-rmse:2.690126
#> [9] train-rmse:0.608626 validation-rmse:2.598687
#> [10] train-rmse:0.432853 validation-rmse:2.520644
#> [11] train-rmse:0.308131 validation-rmse:2.471052
#> [12] train-rmse:0.219510 validation-rmse:2.438977
#> [13] train-rmse:0.156590 validation-rmse:2.418397
#> [14] train-rmse:0.111618 validation-rmse:2.404120
#> [15] train-rmse:0.079494 validation-rmse:2.394442
#> [16] train-rmse:0.056700 validation-rmse:2.387804
#> [17] train-rmse:0.040565 validation-rmse:2.383209
#> [18] train-rmse:0.029106 validation-rmse:2.380012
#> [19] train-rmse:0.020930 validation-rmse:2.377874
#> [20] train-rmse:0.015203 validation-rmse:2.376274
#> [21] train-rmse:0.011182 validation-rmse:2.375147
#> [22] train-rmse:0.008241 validation-rmse:2.374351
#> [23] train-rmse:0.006183 validation-rmse:2.373789
#> [24] train-rmse:0.004717 validation-rmse:2.373401
#> [25] train-rmse:0.003640 validation-rmse:2.373117
#> [26] train-rmse:0.002958 validation-rmse:2.372916
#> [27] train-rmse:0.002383 validation-rmse:2.372777
#> [28] train-rmse:0.001956 validation-rmse:2.372678
#> [29] train-rmse:0.001595 validation-rmse:2.372636
#> [30] train-rmse:0.001364 validation-rmse:2.372607
#> [31] train-rmse:0.001132 validation-rmse:2.372585
#> [32] train-rmse:0.000954 validation-rmse:2.372561
#> [33] train-rmse:0.000783 validation-rmse:2.372559
#> [34] train-rmse:0.000678 validation-rmse:2.372551
#> [35] train-rmse:0.000566 validation-rmse:2.372535
#> [36] train-rmse:0.000476 validation-rmse:2.372521
#> [37] train-rmse:0.000417 validation-rmse:2.372518
#> [38] train-rmse:0.000374 validation-rmse:2.372515
#> [39] train-rmse:0.000332 validation-rmse:2.372508
#> [40] train-rmse:0.000303 validation-rmse:2.372506
#> [41] train-rmse:0.000303 validation-rmse:2.372506
#> [42] train-rmse:0.000303 validation-rmse:2.372506
#> [43] train-rmse:0.000303 validation-rmse:2.372505
#> [44] train-rmse:0.000303 validation-rmse:2.372505
#> [45] train-rmse:0.000303 validation-rmse:2.372505
#> [46] train-rmse:0.000303 validation-rmse:2.372505
#> [47] train-rmse:0.000303 validation-rmse:2.372505
#> [48] train-rmse:0.000303 validation-rmse:2.372505
#> [49] train-rmse:0.000303 validation-rmse:2.372505
#> [50] train-rmse:0.000303 validation-rmse:2.372505
#> [51] train-rmse:0.000303 validation-rmse:2.372505
#> [52] train-rmse:0.000303 validation-rmse:2.372505
#> [53] train-rmse:0.000303 validation-rmse:2.372505
#> [54] train-rmse:0.000303 validation-rmse:2.372505
#> [55] train-rmse:0.000303 validation-rmse:2.372505
#> [56] train-rmse:0.000303 validation-rmse:2.372505
#> [57] train-rmse:0.000303 validation-rmse:2.372505
#> [58] train-rmse:0.000303 validation-rmse:2.372505
#> [59] train-rmse:0.000303 validation-rmse:2.372505
#> [60] train-rmse:0.000303 validation-rmse:2.372505
#> [61] train-rmse:0.000303 validation-rmse:2.372505
#> [62] train-rmse:0.000303 validation-rmse:2.372505
#> [63] train-rmse:0.000303 validation-rmse:2.372505
#> [64] train-rmse:0.000303 validation-rmse:2.372505
#> [65] train-rmse:0.000303 validation-rmse:2.372505
#> [66] train-rmse:0.000303 validation-rmse:2.372505
#> [67] train-rmse:0.000303 validation-rmse:2.372505
#> [68] train-rmse:0.000303 validation-rmse:2.372505
#> [69] train-rmse:0.000303 validation-rmse:2.372505
#> [70] train-rmse:0.000303 validation-rmse:2.372505
#> [1]
#> [1] "Resampling number 2 of 2,"
#> [1]
#> Number of parameters (weights and biases) to estimate: 32
#> Nguyen-Widrow method
#> Scaling factor= 0.7016138
#> gamma= 29.78 alpha= 5.2414 beta= 30325.43
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:8.738645 test-rmse:8.962268
#> [2] train-rmse:6.195705 test-rmse:6.524185
#> [3] train-rmse:4.421688 test-rmse:4.873809
#> [4] train-rmse:3.185898 test-rmse:3.788204
#> [5] train-rmse:2.333125 test-rmse:3.092832
#> [6] train-rmse:1.750869 test-rmse:2.662340
#> [7] train-rmse:1.362937 test-rmse:2.405770
#> [8] train-rmse:1.101573 test-rmse:2.270325
#> [9] train-rmse:0.938830 test-rmse:2.187224
#> [10] train-rmse:0.833056 test-rmse:2.134264
#> [11] train-rmse:0.768550 test-rmse:2.121936
#> [12] train-rmse:0.688411 test-rmse:2.083666
#> [13] train-rmse:0.615916 test-rmse:2.059367
#> [14] train-rmse:0.560035 test-rmse:2.022763
#> [15] train-rmse:0.527946 test-rmse:2.012295
#> [16] train-rmse:0.502968 test-rmse:2.003058
#> [17] train-rmse:0.477129 test-rmse:1.991057
#> [18] train-rmse:0.462241 test-rmse:1.986013
#> [19] train-rmse:0.448583 test-rmse:1.975098
#> [20] train-rmse:0.432517 test-rmse:1.970181
#> [21] train-rmse:0.420589 test-rmse:1.971169
#> [22] train-rmse:0.398948 test-rmse:1.967970
#> [23] train-rmse:0.383930 test-rmse:1.961504
#> [24] train-rmse:0.358391 test-rmse:1.952510
#> [25] train-rmse:0.340437 test-rmse:1.952712
#> [26] train-rmse:0.313560 test-rmse:1.944668
#> [27] train-rmse:0.301421 test-rmse:1.939796
#> [28] train-rmse:0.285848 test-rmse:1.940392
#> [29] train-rmse:0.275196 test-rmse:1.938587
#> [30] train-rmse:0.263511 test-rmse:1.936211
#> [31] train-rmse:0.250043 test-rmse:1.941464
#> [32] train-rmse:0.246654 test-rmse:1.940526
#> [33] train-rmse:0.233410 test-rmse:1.938957
#> [34] train-rmse:0.218645 test-rmse:1.937084
#> [35] train-rmse:0.213320 test-rmse:1.936118
#> [36] train-rmse:0.203508 test-rmse:1.937240
#> [37] train-rmse:0.193397 test-rmse:1.935680
#> [38] train-rmse:0.191504 test-rmse:1.935455
#> [39] train-rmse:0.182430 test-rmse:1.935315
#> [40] train-rmse:0.173820 test-rmse:1.933961
#> [41] train-rmse:0.171633 test-rmse:1.932754
#> [42] train-rmse:0.163372 test-rmse:1.931440
#> [43] train-rmse:0.156264 test-rmse:1.931131
#> [44] train-rmse:0.152842 test-rmse:1.930663
#> [45] train-rmse:0.150778 test-rmse:1.930794
#> [46] train-rmse:0.143058 test-rmse:1.928578
#> [47] train-rmse:0.141701 test-rmse:1.928022
#> [48] train-rmse:0.139211 test-rmse:1.928146
#> [49] train-rmse:0.137050 test-rmse:1.928076
#> [50] train-rmse:0.134470 test-rmse:1.928308
#> [51] train-rmse:0.130320 test-rmse:1.928519
#> [52] train-rmse:0.128167 test-rmse:1.928436
#> [53] train-rmse:0.122503 test-rmse:1.927355
#> [54] train-rmse:0.118706 test-rmse:1.927838
#> [55] train-rmse:0.114547 test-rmse:1.927789
#> [56] train-rmse:0.111248 test-rmse:1.927791
#> [57] train-rmse:0.107185 test-rmse:1.927727
#> [58] train-rmse:0.105089 test-rmse:1.927329
#> [59] train-rmse:0.103508 test-rmse:1.927256
#> [60] train-rmse:0.101067 test-rmse:1.927065
#> [61] train-rmse:0.095303 test-rmse:1.926186
#> [62] train-rmse:0.094680 test-rmse:1.926440
#> [63] train-rmse:0.091680 test-rmse:1.926369
#> [64] train-rmse:0.090478 test-rmse:1.926311
#> [65] train-rmse:0.089331 test-rmse:1.926060
#> [66] train-rmse:0.085803 test-rmse:1.925959
#> [67] train-rmse:0.083367 test-rmse:1.925365
#> [68] train-rmse:0.082439 test-rmse:1.925507
#> [69] train-rmse:0.080571 test-rmse:1.925459
#> [70] train-rmse:0.079213 test-rmse:1.925103
#> [1] train-rmse:8.738645 validation-rmse:9.680377
#> [2] train-rmse:6.195705 validation-rmse:7.042422
#> [3] train-rmse:4.421688 validation-rmse:5.254424
#> [4] train-rmse:3.185898 validation-rmse:4.080691
#> [5] train-rmse:2.333125 validation-rmse:3.336612
#> [6] train-rmse:1.750869 validation-rmse:2.874493
#> [7] train-rmse:1.362937 validation-rmse:2.586548
#> [8] train-rmse:1.101573 validation-rmse:2.436525
#> [9] train-rmse:0.938830 validation-rmse:2.354399
#> [10] train-rmse:0.833056 validation-rmse:2.298675
#> [11] train-rmse:0.768550 validation-rmse:2.286424
#> [12] train-rmse:0.688411 validation-rmse:2.268520
#> [13] train-rmse:0.615916 validation-rmse:2.252877
#> [14] train-rmse:0.560035 validation-rmse:2.216191
#> [15] train-rmse:0.527946 validation-rmse:2.206151
#> [16] train-rmse:0.502968 validation-rmse:2.195800
#> [17] train-rmse:0.477129 validation-rmse:2.191773
#> [18] train-rmse:0.462241 validation-rmse:2.192262
#> [19] train-rmse:0.448583 validation-rmse:2.179632
#> [20] train-rmse:0.432517 validation-rmse:2.179272
#> [21] train-rmse:0.420589 validation-rmse:2.174785
#> [22] train-rmse:0.398948 validation-rmse:2.171080
#> [23] train-rmse:0.383930 validation-rmse:2.167774
#> [24] train-rmse:0.358391 validation-rmse:2.162725
#> [25] train-rmse:0.340437 validation-rmse:2.161737
#> [26] train-rmse:0.313560 validation-rmse:2.158335
#> [27] train-rmse:0.301421 validation-rmse:2.156139
#> [28] train-rmse:0.285848 validation-rmse:2.153127
#> [29] train-rmse:0.275196 validation-rmse:2.152529
#> [30] train-rmse:0.263511 validation-rmse:2.151058
#> [31] train-rmse:0.250043 validation-rmse:2.149360
#> [32] train-rmse:0.246654 validation-rmse:2.149311
#> [33] train-rmse:0.233410 validation-rmse:2.145274
#> [34] train-rmse:0.218645 validation-rmse:2.144474
#> [35] train-rmse:0.213320 validation-rmse:2.142999
#> [36] train-rmse:0.203508 validation-rmse:2.142066
#> [37] train-rmse:0.193397 validation-rmse:2.139504
#> [38] train-rmse:0.191504 validation-rmse:2.138882
#> [39] train-rmse:0.182430 validation-rmse:2.138346
#> [40] train-rmse:0.173820 validation-rmse:2.139200
#> [41] train-rmse:0.171633 validation-rmse:2.139579
#> [42] train-rmse:0.163372 validation-rmse:2.137309
#> [43] train-rmse:0.156264 validation-rmse:2.137722
#> [44] train-rmse:0.152842 validation-rmse:2.139232
#> [45] train-rmse:0.150778 validation-rmse:2.139153
#> [46] train-rmse:0.143058 validation-rmse:2.136790
#> [47] train-rmse:0.141701 validation-rmse:2.137437
#> [48] train-rmse:0.139211 validation-rmse:2.137653
#> [49] train-rmse:0.137050 validation-rmse:2.137825
#> [50] train-rmse:0.134470 validation-rmse:2.138196
#> [51] train-rmse:0.130320 validation-rmse:2.136406
#> [52] train-rmse:0.128167 validation-rmse:2.136163
#> [53] train-rmse:0.122503 validation-rmse:2.135585
#> [54] train-rmse:0.118706 validation-rmse:2.135614
#> [55] train-rmse:0.114547 validation-rmse:2.135365
#> [56] train-rmse:0.111248 validation-rmse:2.134916
#> [57] train-rmse:0.107185 validation-rmse:2.137532
#> [58] train-rmse:0.105089 validation-rmse:2.136873
#> [59] train-rmse:0.103508 validation-rmse:2.136859
#> [60] train-rmse:0.101067 validation-rmse:2.136491
#> [61] train-rmse:0.095303 validation-rmse:2.135405
#> [62] train-rmse:0.094680 validation-rmse:2.136036
#> [63] train-rmse:0.091680 validation-rmse:2.134145
#> [64] train-rmse:0.090478 validation-rmse:2.134049
#> [65] train-rmse:0.089331 validation-rmse:2.133773
#> [66] train-rmse:0.085803 validation-rmse:2.133446
#> [67] train-rmse:0.083367 validation-rmse:2.133034
#> [68] train-rmse:0.082439 validation-rmse:2.132943
#> [69] train-rmse:0.080571 validation-rmse:2.133147
#> [70] train-rmse:0.079213 validation-rmse:2.132921
#> [1]
#> [1] "Working on the Ensembles section"
#> [1]
#> Number of parameters (weights and biases) to estimate: 54
#> Nguyen-Widrow method
#> Scaling factor= 0.7039559
#> gamma= 20.3912 alpha= 5.2031 beta= 7020.167
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:9.376087 test-rmse:8.779320
#> [2] train-rmse:6.645868 test-rmse:6.233693
#> [3] train-rmse:4.712291 test-rmse:4.425151
#> [4] train-rmse:3.342694 test-rmse:3.131631
#> [5] train-rmse:2.371268 test-rmse:2.223300
#> [6] train-rmse:1.682128 test-rmse:1.581149
#> [7] train-rmse:1.194637 test-rmse:1.119443
#> [8] train-rmse:0.849098 test-rmse:0.797855
#> [9] train-rmse:0.602735 test-rmse:0.566688
#> [10] train-rmse:0.429063 test-rmse:0.405164
#> [11] train-rmse:0.304680 test-rmse:0.288181
#> [12] train-rmse:0.216446 test-rmse:0.205199
#> [13] train-rmse:0.153860 test-rmse:0.146353
#> [14] train-rmse:0.109438 test-rmse:0.104212
#> [15] train-rmse:0.077890 test-rmse:0.074567
#> [16] train-rmse:0.055521 test-rmse:0.053221
#> [17] train-rmse:0.039502 test-rmse:0.037881
#> [18] train-rmse:0.028132 test-rmse:0.026969
#> [19] train-rmse:0.020065 test-rmse:0.019236
#> [20] train-rmse:0.014329 test-rmse:0.013702
#> [21] train-rmse:0.010223 test-rmse:0.009792
#> [22] train-rmse:0.007343 test-rmse:0.007044
#> [23] train-rmse:0.005269 test-rmse:0.005128
#> [24] train-rmse:0.003800 test-rmse:0.003820
#> [25] train-rmse:0.002757 test-rmse:0.002758
#> [26] train-rmse:0.002007 test-rmse:0.002014
#> [27] train-rmse:0.001470 test-rmse:0.001494
#> [28] train-rmse:0.001101 test-rmse:0.001162
#> [29] train-rmse:0.000843 test-rmse:0.000919
#> [30] train-rmse:0.000631 test-rmse:0.000778
#> [31] train-rmse:0.000483 test-rmse:0.000652
#> [32] train-rmse:0.000384 test-rmse:0.000560
#> [33] train-rmse:0.000318 test-rmse:0.000514
#> [34] train-rmse:0.000282 test-rmse:0.000490
#> [35] train-rmse:0.000265 test-rmse:0.000482
#> [36] train-rmse:0.000262 test-rmse:0.000482
#> [37] train-rmse:0.000261 test-rmse:0.000482
#> [38] train-rmse:0.000260 test-rmse:0.000483
#> [39] train-rmse:0.000260 test-rmse:0.000484
#> [40] train-rmse:0.000260 test-rmse:0.000484
#> [41] train-rmse:0.000260 test-rmse:0.000484
#> [42] train-rmse:0.000259 test-rmse:0.000485
#> [43] train-rmse:0.000259 test-rmse:0.000485
#> [44] train-rmse:0.000259 test-rmse:0.000485
#> [45] train-rmse:0.000260 test-rmse:0.000485
#> [46] train-rmse:0.000260 test-rmse:0.000485
#> [47] train-rmse:0.000260 test-rmse:0.000485
#> [48] train-rmse:0.000260 test-rmse:0.000485
#> [49] train-rmse:0.000260 test-rmse:0.000485
#> [50] train-rmse:0.000260 test-rmse:0.000485
#> [51] train-rmse:0.000260 test-rmse:0.000485
#> [52] train-rmse:0.000260 test-rmse:0.000485
#> [53] train-rmse:0.000260 test-rmse:0.000485
#> [54] train-rmse:0.000260 test-rmse:0.000485
#> [55] train-rmse:0.000260 test-rmse:0.000485
#> [56] train-rmse:0.000260 test-rmse:0.000485
#> [57] train-rmse:0.000260 test-rmse:0.000485
#> [58] train-rmse:0.000260 test-rmse:0.000485
#> [59] train-rmse:0.000260 test-rmse:0.000485
#> [60] train-rmse:0.000260 test-rmse:0.000485
#> [61] train-rmse:0.000260 test-rmse:0.000485
#> [62] train-rmse:0.000260 test-rmse:0.000485
#> [63] train-rmse:0.000260 test-rmse:0.000485
#> [64] train-rmse:0.000260 test-rmse:0.000485
#> [65] train-rmse:0.000260 test-rmse:0.000485
#> [66] train-rmse:0.000260 test-rmse:0.000485
#> [67] train-rmse:0.000260 test-rmse:0.000485
#> [68] train-rmse:0.000260 test-rmse:0.000485
#> [69] train-rmse:0.000260 test-rmse:0.000486
#> [70] train-rmse:0.000260 test-rmse:0.000486
#> [1] train-rmse:9.376087 validation-rmse:9.046247
#> [2] train-rmse:6.645868 validation-rmse:6.405956
#> [3] train-rmse:4.712291 validation-rmse:4.548027
#> [4] train-rmse:3.342694 validation-rmse:3.232020
#> [5] train-rmse:2.371268 validation-rmse:2.295969
#> [6] train-rmse:1.682128 validation-rmse:1.626775
#> [7] train-rmse:1.194637 validation-rmse:1.157064
#> [8] train-rmse:0.849098 validation-rmse:0.820941
#> [9] train-rmse:0.602735 validation-rmse:0.583282
#> [10] train-rmse:0.429063 validation-rmse:0.415399
#> [11] train-rmse:0.304680 validation-rmse:0.295251
#> [12] train-rmse:0.216446 validation-rmse:0.209928
#> [13] train-rmse:0.153860 validation-rmse:0.149331
#> [14] train-rmse:0.109438 validation-rmse:0.106326
#> [15] train-rmse:0.077890 validation-rmse:0.075690
#> [16] train-rmse:0.055521 validation-rmse:0.053526
#> [17] train-rmse:0.039502 validation-rmse:0.038102
#> [18] train-rmse:0.028132 validation-rmse:0.027148
#> [19] train-rmse:0.020065 validation-rmse:0.019344
#> [20] train-rmse:0.014329 validation-rmse:0.013814
#> [21] train-rmse:0.010223 validation-rmse:0.009853
#> [22] train-rmse:0.007343 validation-rmse:0.007001
#> [23] train-rmse:0.005269 validation-rmse:0.005008
#> [24] train-rmse:0.003800 validation-rmse:0.003628
#> [25] train-rmse:0.002757 validation-rmse:0.002645
#> [26] train-rmse:0.002007 validation-rmse:0.001954
#> [27] train-rmse:0.001470 validation-rmse:0.001457
#> [28] train-rmse:0.001101 validation-rmse:0.001114
#> [29] train-rmse:0.000843 validation-rmse:0.000897
#> [30] train-rmse:0.000631 validation-rmse:0.000727
#> [31] train-rmse:0.000483 validation-rmse:0.000597
#> [32] train-rmse:0.000384 validation-rmse:0.000516
#> [33] train-rmse:0.000318 validation-rmse:0.000468
#> [34] train-rmse:0.000282 validation-rmse:0.000451
#> [35] train-rmse:0.000265 validation-rmse:0.000440
#> [36] train-rmse:0.000262 validation-rmse:0.000441
#> [37] train-rmse:0.000261 validation-rmse:0.000442
#> [38] train-rmse:0.000260 validation-rmse:0.000442
#> [39] train-rmse:0.000260 validation-rmse:0.000443
#> [40] train-rmse:0.000260 validation-rmse:0.000444
#> [41] train-rmse:0.000260 validation-rmse:0.000444
#> [42] train-rmse:0.000259 validation-rmse:0.000444
#> [43] train-rmse:0.000259 validation-rmse:0.000444
#> [44] train-rmse:0.000259 validation-rmse:0.000444
#> [45] train-rmse:0.000260 validation-rmse:0.000445
#> [46] train-rmse:0.000260 validation-rmse:0.000445
#> [47] train-rmse:0.000260 validation-rmse:0.000445
#> [48] train-rmse:0.000260 validation-rmse:0.000445
#> [49] train-rmse:0.000260 validation-rmse:0.000445
#> [50] train-rmse:0.000260 validation-rmse:0.000445
#> [51] train-rmse:0.000260 validation-rmse:0.000445
#> [52] train-rmse:0.000260 validation-rmse:0.000445
#> [53] train-rmse:0.000260 validation-rmse:0.000445
#> [54] train-rmse:0.000260 validation-rmse:0.000445
#> [55] train-rmse:0.000260 validation-rmse:0.000445
#> [56] train-rmse:0.000260 validation-rmse:0.000445
#> [57] train-rmse:0.000260 validation-rmse:0.000445
#> [58] train-rmse:0.000260 validation-rmse:0.000445
#> [59] train-rmse:0.000260 validation-rmse:0.000445
#> [60] train-rmse:0.000260 validation-rmse:0.000445
#> [61] train-rmse:0.000260 validation-rmse:0.000445
#> [62] train-rmse:0.000260 validation-rmse:0.000445
#> [63] train-rmse:0.000260 validation-rmse:0.000445
#> [64] train-rmse:0.000260 validation-rmse:0.000445
#> [65] train-rmse:0.000260 validation-rmse:0.000445
#> [66] train-rmse:0.000260 validation-rmse:0.000445
#> [67] train-rmse:0.000260 validation-rmse:0.000445
#> [68] train-rmse:0.000260 validation-rmse:0.000445
#> [69] train-rmse:0.000260 validation-rmse:0.000445
#> [70] train-rmse:0.000260 validation-rmse:0.000445
#> [1]
#> [1] "0.05 and 0.95 outliers for crim, column number 1"
#> [1] "IQR = 3.61003, 0.05 = 0.02985 0.95 = 15.8603"
#> crim zn indus chas nox rm age dis tax ptratio black lstat
#> 418 25.9406 0 18.1 0 0.679 5.304 89.1 1.6475 666 20.2 127.36 26.64
#> 415 45.7461 0 18.1 0 0.693 4.519 100.0 1.6582 666 20.2 88.27 36.98
#> 441 22.0511 0 18.1 0 0.740 5.818 92.4 1.8662 666 20.2 391.45 22.11
#> 381 88.9762 0 18.1 0 0.671 6.968 91.9 1.4165 666 20.2 396.90 17.21
#> 388 22.5971 0 18.1 0 0.700 5.000 89.5 1.5184 666 20.2 396.90 31.99
#> 419 73.5341 0 18.1 0 0.679 5.957 100.0 1.8026 666 20.2 16.45 20.62
#> 401 25.0461 0 18.1 0 0.693 5.987 100.0 1.5888 666 20.2 396.90 26.77
#> 404 24.8017 0 18.1 0 0.693 5.349 96.0 1.7028 666 20.2 396.90 19.77
#> 411 51.1358 0 18.1 0 0.597 5.757 100.0 1.4130 666 20.2 2.60 10.11
#> 414 28.6558 0 18.1 0 0.597 5.155 100.0 1.5894 666 20.2 210.97 20.08
#> 405 41.5292 0 18.1 0 0.693 5.531 85.4 1.6074 666 20.2 329.46 27.38
#> 399 38.3518 0 18.1 0 0.693 5.453 100.0 1.4896 666 20.2 396.90 30.59
#> 428 37.6619 0 18.1 0 0.679 6.202 78.7 1.8629 666 20.2 18.82 14.52
#> 406 67.9208 0 18.1 0 0.693 5.683 100.0 1.4254 666 20.2 384.97 22.98
#> 379 23.6482 0 18.1 0 0.671 6.380 96.2 1.3861 666 20.2 396.90 23.69
#> 387 24.3938 0 18.1 0 0.700 4.652 100.0 1.4672 666 20.2 396.90 28.28
#> medv y
#> 418 10.4 24
#> 415 7.0 24
#> 441 10.5 24
#> 381 10.4 24
#> 388 7.4 24
#> 419 8.8 24
#> 401 5.6 24
#> 404 8.3 24
#> 411 15.0 24
#> 414 16.3 24
#> 405 8.5 24
#> 399 5.0 24
#> 428 10.9 24
#> 406 5.0 24
#> 379 13.1 24
#> 387 10.5 24
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for zn, column number 2"
#> [1] "IQR = 12.5, 0.05 = 0 0.95 = 80"
#> crim zn indus chas nox rm age dis tax ptratio black lstat medv
#> 58 0.01432 100 1.32 0 0.411 6.816 40.5 8.3248 256 15.1 392.9 3.95 31.6
#> y
#> 58 5
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for indus, column number 3"
#> [1] "IQR = 12.91, 0.05 = 2.18 0.95 = 21.89"
#> [1] crim zn indus chas nox rm age dis tax
#> [10] ptratio black lstat medv y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for chas, column number 4"
#> [1] "IQR = 0, 0.05 = 0 0.95 = 1"
#> [1] crim zn indus chas nox rm age dis tax
#> [10] ptratio black lstat medv y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for nox, column number 5"
#> [1] "IQR = 0.175, 0.05 = 0.409 0.95 = 0.74"
#> [1] crim zn indus chas nox rm age dis tax
#> [10] ptratio black lstat medv y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for rm, column number 6"
#> [1] "IQR = 0.734, 0.05 = 5.304 0.95 = 7.61"
#> crim zn indus chas nox rm age dis tax ptratio black lstat
#> 366 4.55587 0 18.1 0 0.718 3.561 87.9 1.6132 666 20.2 354.70 7.12
#> 407 20.71620 0 18.1 0 0.659 4.138 100.0 1.1781 666 20.2 370.22 23.34
#> 365 3.47428 0 18.1 1 0.718 8.780 82.9 1.9047 666 20.2 354.55 5.29
#> 375 18.49820 0 18.1 0 0.668 4.138 100.0 1.1370 666 20.2 396.90 37.97
#> 226 0.52693 0 6.2 0 0.504 8.725 83.0 2.8944 307 17.4 382.00 4.63
#> 368 13.52220 0 18.1 0 0.631 3.863 100.0 1.5106 666 20.2 131.42 13.33
#> medv y
#> 366 27.5 24
#> 407 11.9 24
#> 365 21.9 24
#> 375 13.8 24
#> 226 50.0 8
#> 368 23.1 24
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for age, column number 7"
#> [1] "IQR = 49.7, 0.05 = 17.7 0.95 = 100"
#> [1] crim zn indus chas nox rm age dis tax
#> [10] ptratio black lstat medv y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for dis, column number 8"
#> [1] "IQR = 3.0298, 0.05 = 1.4608 0.95 = 7.8278"
#> [1] crim zn indus chas nox rm age dis tax
#> [10] ptratio black lstat medv y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for tax, column number 9"
#> [1] "IQR = 385, 0.05 = 222 0.95 = 666"
#> [1] crim zn indus chas nox rm age dis tax
#> [10] ptratio black lstat medv y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for ptratio, column number 10"
#> [1] "IQR = 2.8, 0.05 = 14.7 0.95 = 21"
#> [1] crim zn indus chas nox rm age dis tax
#> [10] ptratio black lstat medv y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for black, column number 11"
#> [1] "IQR = 21, 0.05 = 83.45 0.95 = 396.9"
#> crim zn indus chas nox rm age dis tax ptratio black lstat
#> 412 14.05070 0 18.1 0 0.597 6.657 100.0 1.5275 666 20.2 35.05 21.22
#> 420 11.81230 0 18.1 0 0.718 6.824 76.5 1.7940 666 20.2 48.45 22.74
#> 457 4.66883 0 18.1 0 0.713 5.976 87.9 2.5806 666 20.2 10.48 19.01
#> 451 6.71772 0 18.1 0 0.713 6.749 92.6 2.3236 666 20.2 0.32 17.44
#> 417 10.83420 0 18.1 0 0.679 6.782 90.8 1.8195 666 20.2 21.57 25.79
#> 437 14.42080 0 18.1 0 0.740 6.461 93.3 2.0026 666 20.2 27.49 18.05
#> 467 3.77498 0 18.1 0 0.655 5.952 84.7 2.8715 666 20.2 22.01 17.15
#> 427 12.24720 0 18.1 0 0.584 5.837 59.7 1.9976 666 20.2 24.65 15.69
#> 446 10.67180 0 18.1 0 0.740 6.459 94.8 1.9879 666 20.2 43.06 23.98
#> 426 15.86030 0 18.1 0 0.679 5.896 95.4 1.9096 666 20.2 7.68 24.39
#> 419 73.53410 0 18.1 0 0.679 5.957 100.0 1.8026 666 20.2 16.45 20.62
#> 413 18.81100 0 18.1 0 0.597 4.628 100.0 1.5539 666 20.2 28.79 34.37
#> 424 7.05042 0 18.1 0 0.614 6.103 85.1 2.0218 666 20.2 2.52 23.29
#> 458 8.20058 0 18.1 0 0.713 5.936 80.3 2.7792 666 20.2 3.50 16.94
#> 438 15.17720 0 18.1 0 0.740 6.152 100.0 1.9142 666 20.2 9.32 26.45
#> 411 51.13580 0 18.1 0 0.597 5.757 100.0 1.4130 666 20.2 2.60 10.11
#> 456 4.75237 0 18.1 0 0.713 6.525 86.5 2.4358 666 20.2 50.92 18.13
#> 428 37.66190 0 18.1 0 0.679 6.202 78.7 1.8629 666 20.2 18.82 14.52
#> 455 9.51363 0 18.1 0 0.713 6.728 94.1 2.4961 666 20.2 6.68 18.71
#> 425 8.79212 0 18.1 0 0.584 5.565 70.6 2.0635 666 20.2 3.65 17.16
#> 416 18.08460 0 18.1 0 0.679 6.434 100.0 1.8347 666 20.2 27.25 29.05
#> medv y
#> 412 17.2 24
#> 420 8.4 24
#> 457 12.7 24
#> 451 13.4 24
#> 417 7.5 24
#> 437 9.6 24
#> 467 19.0 24
#> 427 10.2 24
#> 446 11.8 24
#> 426 8.3 24
#> 419 8.8 24
#> 413 17.9 24
#> 424 13.4 24
#> 458 13.5 24
#> 438 8.7 24
#> 411 15.0 24
#> 456 14.1 24
#> 428 10.9 24
#> 455 14.9 24
#> 425 11.7 24
#> 416 7.2 24
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for lstat, column number 12"
#> [1] "IQR = 9.95, 0.05 = 3.73 0.95 = 26.82"
#> [1] crim zn indus chas nox rm age dis tax
#> [10] ptratio black lstat medv y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for medv, column number 13"
#> [1] "IQR = 8.2, 0.05 = 10.2 0.95 = 43.5"
#> [1] crim zn indus chas nox rm age dis tax
#> [10] ptratio black lstat medv y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for y, column number 14"
#> [1] "IQR = 20, 0.05 = 2 0.95 = 24"
#> [1] crim zn indus chas nox rm age dis tax
#> [10] ptratio black lstat medv y
#> <0 rows> (or 0-length row.names)
#> [1]
#> $head_of_data
#>
#> $accuracy_plot
#>
#> $overfitting_plot
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_hline()`).
#>
#> $histograms
#>
#> $boxplots
#>
#> $predictor_vs_target
#>
#> $final_results_table
#>
#> $data_correlation
#>
#> $data_summary
#>
#> $head_of_ensemble
#>
#> $ensemble_correlation
#>
#> $accuracy_barchart
#>
#> $train_vs_holdout
#>
#> $duration_barchart
#>
#> $overfitting_barchart
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_col()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_text()`).
#>
#> $bias_barchart
#>
#> $MSE_barchart
#>
#> $MAE_barchart
#>
#> $SSE_barchart
#>
#> $bias_plot
#>
#> $MSE_plot
#>
#> $MAE_plot
#>
#> $SSE_plot
#>
#> $colnum
#> [1] 9
#>
#> $numresamples
#> [1] 2
#>
#> $save_all_trained_modesl
#> [1] "N"
#>
#> $remove_ensemble_correlations_greater_than
#> [1] 1
#>
#> $train_amount
#> [1] 0.6
#>
#> $test_amount
#> [1] 0.2
#>
#> $validation_amount
#> [1] 0.2
#>
Numeric(data = Concrete,
colnum = 9,
numresamples = 2,
how_to_handle_strings = 1,
do_you_have_new_data = "N",
save_all_trained_models = "N",
remove_ensemble_correlations_greater_than = 1.00,
train_amount = 0.60,
test_amount = 0.20,
validation_amount = 0.20
)
#> [1]
#> [1] "Resampling number 1 of 2,"
#> [1]
#> Number of parameters (weights and biases) to estimate: 22
#> Nguyen-Widrow method
#> Scaling factor= 0.700778
#> gamma= 20.298 alpha= 0.8999 beta= 31823.19
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:28.156014 test-rmse:29.357132
#> [2] train-rmse:20.793623 test-rmse:21.985201
#> [3] train-rmse:15.854875 test-rmse:16.988702
#> [4] train-rmse:12.491504 test-rmse:13.456017
#> [5] train-rmse:10.183531 test-rmse:11.162809
#> [6] train-rmse:8.700776 test-rmse:9.666788
#> [7] train-rmse:7.627652 test-rmse:8.735408
#> [8] train-rmse:6.988465 test-rmse:8.019862
#> [9] train-rmse:6.525406 test-rmse:7.495719
#> [10] train-rmse:6.160006 test-rmse:7.182125
#> [11] train-rmse:5.860499 test-rmse:6.768465
#> [12] train-rmse:5.670437 test-rmse:6.651993
#> [13] train-rmse:5.424882 test-rmse:6.486878
#> [14] train-rmse:5.176786 test-rmse:6.275520
#> [15] train-rmse:5.057829 test-rmse:6.184321
#> [16] train-rmse:4.937419 test-rmse:6.113025
#> [17] train-rmse:4.810499 test-rmse:6.066141
#> [18] train-rmse:4.742767 test-rmse:5.964951
#> [19] train-rmse:4.629604 test-rmse:5.919063
#> [20] train-rmse:4.580290 test-rmse:5.882150
#> [21] train-rmse:4.488725 test-rmse:5.794250
#> [22] train-rmse:4.403780 test-rmse:5.787696
#> [23] train-rmse:4.315082 test-rmse:5.683733
#> [24] train-rmse:4.218905 test-rmse:5.666932
#> [25] train-rmse:4.185655 test-rmse:5.628863
#> [26] train-rmse:4.134825 test-rmse:5.594383
#> [27] train-rmse:4.062384 test-rmse:5.553695
#> [28] train-rmse:4.000254 test-rmse:5.507128
#> [29] train-rmse:3.977797 test-rmse:5.468068
#> [30] train-rmse:3.930854 test-rmse:5.461368
#> [31] train-rmse:3.885543 test-rmse:5.424561
#> [32] train-rmse:3.816028 test-rmse:5.381706
#> [33] train-rmse:3.753209 test-rmse:5.387141
#> [34] train-rmse:3.736999 test-rmse:5.360133
#> [35] train-rmse:3.694668 test-rmse:5.336348
#> [36] train-rmse:3.667206 test-rmse:5.326707
#> [37] train-rmse:3.637384 test-rmse:5.288336
#> [38] train-rmse:3.609748 test-rmse:5.274210
#> [39] train-rmse:3.586273 test-rmse:5.233952
#> [40] train-rmse:3.533768 test-rmse:5.210992
#> [41] train-rmse:3.506336 test-rmse:5.212386
#> [42] train-rmse:3.489072 test-rmse:5.201091
#> [43] train-rmse:3.438863 test-rmse:5.173969
#> [44] train-rmse:3.406678 test-rmse:5.160806
#> [45] train-rmse:3.372448 test-rmse:5.159036
#> [46] train-rmse:3.358391 test-rmse:5.150706
#> [47] train-rmse:3.317797 test-rmse:5.114362
#> [48] train-rmse:3.312103 test-rmse:5.098961
#> [49] train-rmse:3.266421 test-rmse:5.076859
#> [50] train-rmse:3.234406 test-rmse:5.073200
#> [51] train-rmse:3.220228 test-rmse:5.065399
#> [52] train-rmse:3.209395 test-rmse:5.046887
#> [53] train-rmse:3.196762 test-rmse:5.022670
#> [54] train-rmse:3.171610 test-rmse:5.014254
#> [55] train-rmse:3.159932 test-rmse:5.007352
#> [56] train-rmse:3.118847 test-rmse:4.998491
#> [57] train-rmse:3.094442 test-rmse:4.971415
#> [58] train-rmse:3.069675 test-rmse:4.971465
#> [59] train-rmse:3.038636 test-rmse:4.922120
#> [60] train-rmse:3.012390 test-rmse:4.889162
#> [61] train-rmse:3.007638 test-rmse:4.885466
#> [62] train-rmse:2.986033 test-rmse:4.882548
#> [63] train-rmse:2.977695 test-rmse:4.876943
#> [64] train-rmse:2.958482 test-rmse:4.855552
#> [65] train-rmse:2.953726 test-rmse:4.853204
#> [66] train-rmse:2.948603 test-rmse:4.839701
#> [67] train-rmse:2.939267 test-rmse:4.832801
#> [68] train-rmse:2.925462 test-rmse:4.815781
#> [69] train-rmse:2.909066 test-rmse:4.815496
#> [70] train-rmse:2.896814 test-rmse:4.806214
#> [1] train-rmse:28.156014 validation-rmse:28.766506
#> [2] train-rmse:20.793623 validation-rmse:21.549708
#> [3] train-rmse:15.854875 validation-rmse:16.824925
#> [4] train-rmse:12.491504 validation-rmse:13.534748
#> [5] train-rmse:10.183531 validation-rmse:11.266754
#> [6] train-rmse:8.700776 validation-rmse:9.888971
#> [7] train-rmse:7.627652 validation-rmse:8.750028
#> [8] train-rmse:6.988465 validation-rmse:8.129846
#> [9] train-rmse:6.525406 validation-rmse:7.675947
#> [10] train-rmse:6.160006 validation-rmse:7.258871
#> [11] train-rmse:5.860499 validation-rmse:6.996944
#> [12] train-rmse:5.670437 validation-rmse:6.854641
#> [13] train-rmse:5.424882 validation-rmse:6.691655
#> [14] train-rmse:5.176786 validation-rmse:6.541539
#> [15] train-rmse:5.057829 validation-rmse:6.427097
#> [16] train-rmse:4.937419 validation-rmse:6.300546
#> [17] train-rmse:4.810499 validation-rmse:6.248984
#> [18] train-rmse:4.742767 validation-rmse:6.190668
#> [19] train-rmse:4.629604 validation-rmse:6.178513
#> [20] train-rmse:4.580290 validation-rmse:6.115877
#> [21] train-rmse:4.488725 validation-rmse:6.059504
#> [22] train-rmse:4.403780 validation-rmse:6.009024
#> [23] train-rmse:4.315082 validation-rmse:5.950120
#> [24] train-rmse:4.218905 validation-rmse:5.918681
#> [25] train-rmse:4.185655 validation-rmse:5.900909
#> [26] train-rmse:4.134825 validation-rmse:5.882598
#> [27] train-rmse:4.062384 validation-rmse:5.931404
#> [28] train-rmse:4.000254 validation-rmse:5.927863
#> [29] train-rmse:3.977797 validation-rmse:5.901262
#> [30] train-rmse:3.930854 validation-rmse:5.870582
#> [31] train-rmse:3.885543 validation-rmse:5.847163
#> [32] train-rmse:3.816028 validation-rmse:5.786169
#> [33] train-rmse:3.753209 validation-rmse:5.732523
#> [34] train-rmse:3.736999 validation-rmse:5.725346
#> [35] train-rmse:3.694668 validation-rmse:5.672990
#> [36] train-rmse:3.667206 validation-rmse:5.661063
#> [37] train-rmse:3.637384 validation-rmse:5.613938
#> [38] train-rmse:3.609748 validation-rmse:5.573009
#> [39] train-rmse:3.586273 validation-rmse:5.562829
#> [40] train-rmse:3.533768 validation-rmse:5.522973
#> [41] train-rmse:3.506336 validation-rmse:5.540372
#> [42] train-rmse:3.489072 validation-rmse:5.527893
#> [43] train-rmse:3.438863 validation-rmse:5.511447
#> [44] train-rmse:3.406678 validation-rmse:5.467687
#> [45] train-rmse:3.372448 validation-rmse:5.433576
#> [46] train-rmse:3.358391 validation-rmse:5.397279
#> [47] train-rmse:3.317797 validation-rmse:5.386522
#> [48] train-rmse:3.312103 validation-rmse:5.381718
#> [49] train-rmse:3.266421 validation-rmse:5.357289
#> [50] train-rmse:3.234406 validation-rmse:5.333181
#> [51] train-rmse:3.220228 validation-rmse:5.312136
#> [52] train-rmse:3.209395 validation-rmse:5.299967
#> [53] train-rmse:3.196762 validation-rmse:5.291840
#> [54] train-rmse:3.171610 validation-rmse:5.269127
#> [55] train-rmse:3.159932 validation-rmse:5.278459
#> [56] train-rmse:3.118847 validation-rmse:5.274917
#> [57] train-rmse:3.094442 validation-rmse:5.267086
#> [58] train-rmse:3.069675 validation-rmse:5.261168
#> [59] train-rmse:3.038636 validation-rmse:5.236191
#> [60] train-rmse:3.012390 validation-rmse:5.212652
#> [61] train-rmse:3.007638 validation-rmse:5.201974
#> [62] train-rmse:2.986033 validation-rmse:5.183938
#> [63] train-rmse:2.977695 validation-rmse:5.159170
#> [64] train-rmse:2.958482 validation-rmse:5.151190
#> [65] train-rmse:2.953726 validation-rmse:5.156280
#> [66] train-rmse:2.948603 validation-rmse:5.155305
#> [67] train-rmse:2.939267 validation-rmse:5.140646
#> [68] train-rmse:2.925462 validation-rmse:5.109831
#> [69] train-rmse:2.909066 validation-rmse:5.096382
#> [70] train-rmse:2.896814 validation-rmse:5.082329
#> [1]
#> [1] "Working on the Ensembles section"
#> [1]
#> Number of parameters (weights and biases) to estimate: 54
#> Nguyen-Widrow method
#> Scaling factor= 0.7021598
#> gamma= 31.9848 alpha= 5.7143 beta= 9743.533
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:27.490253 test-rmse:32.110180
#> [2] train-rmse:19.558215 test-rmse:23.024254
#> [3] train-rmse:13.936605 test-rmse:16.801325
#> [4] train-rmse:9.951791 test-rmse:12.339297
#> [5] train-rmse:7.121978 test-rmse:9.192099
#> [6] train-rmse:5.123720 test-rmse:6.926699
#> [7] train-rmse:3.713473 test-rmse:5.307099
#> [8] train-rmse:2.716069 test-rmse:4.174380
#> [9] train-rmse:2.019558 test-rmse:3.383045
#> [10] train-rmse:1.531869 test-rmse:2.765209
#> [11] train-rmse:1.193242 test-rmse:2.337801
#> [12] train-rmse:0.964571 test-rmse:2.032874
#> [13] train-rmse:0.816436 test-rmse:1.816452
#> [14] train-rmse:0.707675 test-rmse:1.653757
#> [15] train-rmse:0.644266 test-rmse:1.527340
#> [16] train-rmse:0.602184 test-rmse:1.436284
#> [17] train-rmse:0.572778 test-rmse:1.354176
#> [18] train-rmse:0.548802 test-rmse:1.294636
#> [19] train-rmse:0.533587 test-rmse:1.241212
#> [20] train-rmse:0.523081 test-rmse:1.196474
#> [21] train-rmse:0.512875 test-rmse:1.157068
#> [22] train-rmse:0.469597 test-rmse:1.117024
#> [23] train-rmse:0.448667 test-rmse:1.112162
#> [24] train-rmse:0.437089 test-rmse:1.118983
#> [25] train-rmse:0.426563 test-rmse:1.125512
#> [26] train-rmse:0.418797 test-rmse:1.124236
#> [27] train-rmse:0.413098 test-rmse:1.108128
#> [28] train-rmse:0.406378 test-rmse:1.104672
#> [29] train-rmse:0.403685 test-rmse:1.106245
#> [30] train-rmse:0.399835 test-rmse:1.096319
#> [31] train-rmse:0.379717 test-rmse:1.093922
#> [32] train-rmse:0.361163 test-rmse:1.074444
#> [33] train-rmse:0.350319 test-rmse:1.079178
#> [34] train-rmse:0.340864 test-rmse:1.080193
#> [35] train-rmse:0.333396 test-rmse:1.084256
#> [36] train-rmse:0.331224 test-rmse:1.086860
#> [37] train-rmse:0.326091 test-rmse:1.088102
#> [38] train-rmse:0.315882 test-rmse:1.087176
#> [39] train-rmse:0.305440 test-rmse:1.080749
#> [40] train-rmse:0.301023 test-rmse:1.080965
#> [41] train-rmse:0.291808 test-rmse:1.090366
#> [42] train-rmse:0.289468 test-rmse:1.088508
#> [43] train-rmse:0.282597 test-rmse:1.090912
#> [44] train-rmse:0.273315 test-rmse:1.093411
#> [45] train-rmse:0.264857 test-rmse:1.096418
#> [46] train-rmse:0.260963 test-rmse:1.096640
#> [47] train-rmse:0.256982 test-rmse:1.091538
#> [48] train-rmse:0.249661 test-rmse:1.083645
#> [49] train-rmse:0.242181 test-rmse:1.083899
#> [50] train-rmse:0.237483 test-rmse:1.083393
#> [51] train-rmse:0.229399 test-rmse:1.083261
#> [52] train-rmse:0.221265 test-rmse:1.082576
#> [53] train-rmse:0.216973 test-rmse:1.076144
#> [54] train-rmse:0.213414 test-rmse:1.078512
#> [55] train-rmse:0.209591 test-rmse:1.080571
#> [56] train-rmse:0.202592 test-rmse:1.084269
#> [57] train-rmse:0.199142 test-rmse:1.083227
#> [58] train-rmse:0.193955 test-rmse:1.081980
#> [59] train-rmse:0.192757 test-rmse:1.082425
#> [60] train-rmse:0.187457 test-rmse:1.080489
#> [61] train-rmse:0.183877 test-rmse:1.084618
#> [62] train-rmse:0.182924 test-rmse:1.084870
#> [63] train-rmse:0.175178 test-rmse:1.084432
#> [64] train-rmse:0.171186 test-rmse:1.084823
#> [65] train-rmse:0.166326 test-rmse:1.085440
#> [66] train-rmse:0.162886 test-rmse:1.086030
#> [67] train-rmse:0.160580 test-rmse:1.084897
#> [68] train-rmse:0.158615 test-rmse:1.085340
#> [69] train-rmse:0.155855 test-rmse:1.085073
#> [70] train-rmse:0.154191 test-rmse:1.085547
#> [1] train-rmse:27.490253 validation-rmse:25.832173
#> [2] train-rmse:19.558215 validation-rmse:18.355732
#> [3] train-rmse:13.936605 validation-rmse:13.135081
#> [4] train-rmse:9.951791 validation-rmse:9.420312
#> [5] train-rmse:7.121978 validation-rmse:6.777679
#> [6] train-rmse:5.123720 validation-rmse:4.899107
#> [7] train-rmse:3.713473 validation-rmse:3.475300
#> [8] train-rmse:2.716069 validation-rmse:2.542512
#> [9] train-rmse:2.019558 validation-rmse:1.902286
#> [10] train-rmse:1.531869 validation-rmse:1.435990
#> [11] train-rmse:1.193242 validation-rmse:1.122528
#> [12] train-rmse:0.964571 validation-rmse:0.938779
#> [13] train-rmse:0.816436 validation-rmse:0.843285
#> [14] train-rmse:0.707675 validation-rmse:0.773036
#> [15] train-rmse:0.644266 validation-rmse:0.744429
#> [16] train-rmse:0.602184 validation-rmse:0.728132
#> [17] train-rmse:0.572778 validation-rmse:0.715053
#> [18] train-rmse:0.548802 validation-rmse:0.714295
#> [19] train-rmse:0.533587 validation-rmse:0.715438
#> [20] train-rmse:0.523081 validation-rmse:0.720634
#> [21] train-rmse:0.512875 validation-rmse:0.722798
#> [22] train-rmse:0.469597 validation-rmse:0.707083
#> [23] train-rmse:0.448667 validation-rmse:0.704372
#> [24] train-rmse:0.437089 validation-rmse:0.706460
#> [25] train-rmse:0.426563 validation-rmse:0.706398
#> [26] train-rmse:0.418797 validation-rmse:0.706390
#> [27] train-rmse:0.413098 validation-rmse:0.700413
#> [28] train-rmse:0.406378 validation-rmse:0.696495
#> [29] train-rmse:0.403685 validation-rmse:0.697881
#> [30] train-rmse:0.399835 validation-rmse:0.693662
#> [31] train-rmse:0.379717 validation-rmse:0.705246
#> [32] train-rmse:0.361163 validation-rmse:0.696453
#> [33] train-rmse:0.350319 validation-rmse:0.696227
#> [34] train-rmse:0.340864 validation-rmse:0.691150
#> [35] train-rmse:0.333396 validation-rmse:0.686003
#> [36] train-rmse:0.331224 validation-rmse:0.684729
#> [37] train-rmse:0.326091 validation-rmse:0.687483
#> [38] train-rmse:0.315882 validation-rmse:0.684522
#> [39] train-rmse:0.305440 validation-rmse:0.684353
#> [40] train-rmse:0.301023 validation-rmse:0.688229
#> [41] train-rmse:0.291808 validation-rmse:0.695766
#> [42] train-rmse:0.289468 validation-rmse:0.693845
#> [43] train-rmse:0.282597 validation-rmse:0.691226
#> [44] train-rmse:0.273315 validation-rmse:0.691492
#> [45] train-rmse:0.264857 validation-rmse:0.687181
#> [46] train-rmse:0.260963 validation-rmse:0.686692
#> [47] train-rmse:0.256982 validation-rmse:0.688989
#> [48] train-rmse:0.249661 validation-rmse:0.690580
#> [49] train-rmse:0.242181 validation-rmse:0.686478
#> [50] train-rmse:0.237483 validation-rmse:0.687052
#> [51] train-rmse:0.229399 validation-rmse:0.690236
#> [52] train-rmse:0.221265 validation-rmse:0.685109
#> [53] train-rmse:0.216973 validation-rmse:0.683308
#> [54] train-rmse:0.213414 validation-rmse:0.679973
#> [55] train-rmse:0.209591 validation-rmse:0.677573
#> [56] train-rmse:0.202592 validation-rmse:0.681131
#> [57] train-rmse:0.199142 validation-rmse:0.681584
#> [58] train-rmse:0.193955 validation-rmse:0.677566
#> [59] train-rmse:0.192757 validation-rmse:0.678277
#> [60] train-rmse:0.187457 validation-rmse:0.674246
#> [61] train-rmse:0.183877 validation-rmse:0.675511
#> [62] train-rmse:0.182924 validation-rmse:0.675499
#> [63] train-rmse:0.175178 validation-rmse:0.675942
#> [64] train-rmse:0.171186 validation-rmse:0.673677
#> [65] train-rmse:0.166326 validation-rmse:0.673058
#> [66] train-rmse:0.162886 validation-rmse:0.675800
#> [67] train-rmse:0.160580 validation-rmse:0.678668
#> [68] train-rmse:0.158615 validation-rmse:0.680918
#> [69] train-rmse:0.155855 validation-rmse:0.681095
#> [70] train-rmse:0.154191 validation-rmse:0.681015
#> [1]
#> [1] "Resampling number 2 of 2,"
#> [1]
#> Number of parameters (weights and biases) to estimate: 22
#> Nguyen-Widrow method
#> Scaling factor= 0.700792
#> gamma= 21.2287 alpha= 2.1925 beta= 33946.27
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:28.277515 test-rmse:30.110736
#> [2] train-rmse:20.882298 test-rmse:22.909510
#> [3] train-rmse:15.861562 test-rmse:17.687938
#> [4] train-rmse:12.416475 test-rmse:14.354190
#> [5] train-rmse:10.154284 test-rmse:11.968632
#> [6] train-rmse:8.625273 test-rmse:10.431818
#> [7] train-rmse:7.551258 test-rmse:9.536969
#> [8] train-rmse:6.871107 test-rmse:9.020395
#> [9] train-rmse:6.332399 test-rmse:8.417406
#> [10] train-rmse:5.877929 test-rmse:8.003831
#> [11] train-rmse:5.583256 test-rmse:7.686173
#> [12] train-rmse:5.357461 test-rmse:7.521732
#> [13] train-rmse:5.177813 test-rmse:7.340959
#> [14] train-rmse:4.978508 test-rmse:7.150607
#> [15] train-rmse:4.891479 test-rmse:7.080080
#> [16] train-rmse:4.744884 test-rmse:6.993264
#> [17] train-rmse:4.662214 test-rmse:6.927061
#> [18] train-rmse:4.525520 test-rmse:6.818803
#> [19] train-rmse:4.433407 test-rmse:6.772593
#> [20] train-rmse:4.314952 test-rmse:6.727294
#> [21] train-rmse:4.243320 test-rmse:6.732334
#> [22] train-rmse:4.144099 test-rmse:6.719835
#> [23] train-rmse:4.063544 test-rmse:6.602092
#> [24] train-rmse:4.032903 test-rmse:6.586293
#> [25] train-rmse:3.964202 test-rmse:6.519383
#> [26] train-rmse:3.883034 test-rmse:6.482921
#> [27] train-rmse:3.811984 test-rmse:6.463596
#> [28] train-rmse:3.737798 test-rmse:6.441969
#> [29] train-rmse:3.705417 test-rmse:6.415060
#> [30] train-rmse:3.656080 test-rmse:6.369394
#> [31] train-rmse:3.622331 test-rmse:6.383491
#> [32] train-rmse:3.596775 test-rmse:6.354649
#> [33] train-rmse:3.535104 test-rmse:6.342884
#> [34] train-rmse:3.470141 test-rmse:6.274613
#> [35] train-rmse:3.448252 test-rmse:6.260318
#> [36] train-rmse:3.398844 test-rmse:6.243118
#> [37] train-rmse:3.345566 test-rmse:6.214722
#> [38] train-rmse:3.318247 test-rmse:6.220437
#> [39] train-rmse:3.283239 test-rmse:6.211611
#> [40] train-rmse:3.242139 test-rmse:6.198491
#> [41] train-rmse:3.207767 test-rmse:6.172190
#> [42] train-rmse:3.172318 test-rmse:6.153572
#> [43] train-rmse:3.163499 test-rmse:6.123451
#> [44] train-rmse:3.137443 test-rmse:6.125308
#> [45] train-rmse:3.107297 test-rmse:6.103262
#> [46] train-rmse:3.066038 test-rmse:6.100053
#> [47] train-rmse:3.029899 test-rmse:6.118519
#> [48] train-rmse:3.012599 test-rmse:6.106778
#> [49] train-rmse:2.989105 test-rmse:6.088090
#> [50] train-rmse:2.964442 test-rmse:6.079593
#> [51] train-rmse:2.950532 test-rmse:6.026705
#> [52] train-rmse:2.943438 test-rmse:6.020102
#> [53] train-rmse:2.897171 test-rmse:6.008564
#> [54] train-rmse:2.868461 test-rmse:6.022581
#> [55] train-rmse:2.831478 test-rmse:5.998804
#> [56] train-rmse:2.796692 test-rmse:5.970912
#> [57] train-rmse:2.774764 test-rmse:5.956661
#> [58] train-rmse:2.756135 test-rmse:5.926415
#> [59] train-rmse:2.735932 test-rmse:5.895210
#> [60] train-rmse:2.720280 test-rmse:5.897837
#> [61] train-rmse:2.690177 test-rmse:5.888642
#> [62] train-rmse:2.667120 test-rmse:5.907702
#> [63] train-rmse:2.660390 test-rmse:5.873050
#> [64] train-rmse:2.650320 test-rmse:5.864219
#> [65] train-rmse:2.618622 test-rmse:5.843714
#> [66] train-rmse:2.585151 test-rmse:5.832271
#> [67] train-rmse:2.563087 test-rmse:5.823397
#> [68] train-rmse:2.542392 test-rmse:5.800316
#> [69] train-rmse:2.530878 test-rmse:5.800596
#> [70] train-rmse:2.514367 test-rmse:5.792994
#> [1] train-rmse:28.277515 validation-rmse:27.653738
#> [2] train-rmse:20.882298 validation-rmse:20.340348
#> [3] train-rmse:15.861562 validation-rmse:15.133447
#> [4] train-rmse:12.416475 validation-rmse:11.908552
#> [5] train-rmse:10.154284 validation-rmse:9.762877
#> [6] train-rmse:8.625273 validation-rmse:8.445147
#> [7] train-rmse:7.551258 validation-rmse:7.810153
#> [8] train-rmse:6.871107 validation-rmse:7.325729
#> [9] train-rmse:6.332399 validation-rmse:6.861437
#> [10] train-rmse:5.877929 validation-rmse:6.485108
#> [11] train-rmse:5.583256 validation-rmse:6.306457
#> [12] train-rmse:5.357461 validation-rmse:6.173198
#> [13] train-rmse:5.177813 validation-rmse:6.126119
#> [14] train-rmse:4.978508 validation-rmse:6.047606
#> [15] train-rmse:4.891479 validation-rmse:6.029571
#> [16] train-rmse:4.744884 validation-rmse:5.899302
#> [17] train-rmse:4.662214 validation-rmse:5.859948
#> [18] train-rmse:4.525520 validation-rmse:5.805710
#> [19] train-rmse:4.433407 validation-rmse:5.781996
#> [20] train-rmse:4.314952 validation-rmse:5.726479
#> [21] train-rmse:4.243320 validation-rmse:5.755665
#> [22] train-rmse:4.144099 validation-rmse:5.685680
#> [23] train-rmse:4.063544 validation-rmse:5.590271
#> [24] train-rmse:4.032903 validation-rmse:5.559487
#> [25] train-rmse:3.964202 validation-rmse:5.535275
#> [26] train-rmse:3.883034 validation-rmse:5.459853
#> [27] train-rmse:3.811984 validation-rmse:5.436963
#> [28] train-rmse:3.737798 validation-rmse:5.461953
#> [29] train-rmse:3.705417 validation-rmse:5.446077
#> [30] train-rmse:3.656080 validation-rmse:5.417037
#> [31] train-rmse:3.622331 validation-rmse:5.426145
#> [32] train-rmse:3.596775 validation-rmse:5.404401
#> [33] train-rmse:3.535104 validation-rmse:5.368163
#> [34] train-rmse:3.470141 validation-rmse:5.345927
#> [35] train-rmse:3.448252 validation-rmse:5.321346
#> [36] train-rmse:3.398844 validation-rmse:5.290650
#> [37] train-rmse:3.345566 validation-rmse:5.240283
#> [38] train-rmse:3.318247 validation-rmse:5.230765
#> [39] train-rmse:3.283239 validation-rmse:5.199362
#> [40] train-rmse:3.242139 validation-rmse:5.190153
#> [41] train-rmse:3.207767 validation-rmse:5.178812
#> [42] train-rmse:3.172318 validation-rmse:5.139200
#> [43] train-rmse:3.163499 validation-rmse:5.130420
#> [44] train-rmse:3.137443 validation-rmse:5.130609
#> [45] train-rmse:3.107297 validation-rmse:5.094120
#> [46] train-rmse:3.066038 validation-rmse:5.083214
#> [47] train-rmse:3.029899 validation-rmse:5.061869
#> [48] train-rmse:3.012599 validation-rmse:5.057425
#> [49] train-rmse:2.989105 validation-rmse:5.037051
#> [50] train-rmse:2.964442 validation-rmse:5.044261
#> [51] train-rmse:2.950532 validation-rmse:5.033783
#> [52] train-rmse:2.943438 validation-rmse:5.023906
#> [53] train-rmse:2.897171 validation-rmse:5.023613
#> [54] train-rmse:2.868461 validation-rmse:4.998746
#> [55] train-rmse:2.831478 validation-rmse:4.984926
#> [56] train-rmse:2.796692 validation-rmse:4.953176
#> [57] train-rmse:2.774764 validation-rmse:4.952464
#> [58] train-rmse:2.756135 validation-rmse:4.949343
#> [59] train-rmse:2.735932 validation-rmse:4.960286
#> [60] train-rmse:2.720280 validation-rmse:4.944878
#> [61] train-rmse:2.690177 validation-rmse:4.914419
#> [62] train-rmse:2.667120 validation-rmse:4.938247
#> [63] train-rmse:2.660390 validation-rmse:4.931497
#> [64] train-rmse:2.650320 validation-rmse:4.935461
#> [65] train-rmse:2.618622 validation-rmse:4.923912
#> [66] train-rmse:2.585151 validation-rmse:4.908638
#> [67] train-rmse:2.563087 validation-rmse:4.895000
#> [68] train-rmse:2.542392 validation-rmse:4.890032
#> [69] train-rmse:2.530878 validation-rmse:4.878960
#> [70] train-rmse:2.514367 validation-rmse:4.868972
#> [1]
#> [1] "Working on the Ensembles section"
#> [1]
#> Number of parameters (weights and biases) to estimate: 54
#> Nguyen-Widrow method
#> Scaling factor= 0.7019513
#> gamma= 36.4663 alpha= 5.3812 beta= 11184.78
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:27.621312 test-rmse:28.688884
#> [2] train-rmse:19.650639 test-rmse:20.879178
#> [3] train-rmse:13.990256 test-rmse:15.195529
#> [4] train-rmse:9.993941 test-rmse:11.136352
#> [5] train-rmse:7.154531 test-rmse:8.200107
#> [6] train-rmse:5.145060 test-rmse:6.087887
#> [7] train-rmse:3.721034 test-rmse:4.573770
#> [8] train-rmse:2.710030 test-rmse:3.495397
#> [9] train-rmse:2.000963 test-rmse:2.730296
#> [10] train-rmse:1.507348 test-rmse:2.199597
#> [11] train-rmse:1.168745 test-rmse:1.825602
#> [12] train-rmse:0.941016 test-rmse:1.592568
#> [13] train-rmse:0.787837 test-rmse:1.429696
#> [14] train-rmse:0.679847 test-rmse:1.304266
#> [15] train-rmse:0.612114 test-rmse:1.235998
#> [16] train-rmse:0.558899 test-rmse:1.175557
#> [17] train-rmse:0.528627 test-rmse:1.125119
#> [18] train-rmse:0.508236 test-rmse:1.085925
#> [19] train-rmse:0.479501 test-rmse:1.058625
#> [20] train-rmse:0.463613 test-rmse:1.052005
#> [21] train-rmse:0.447158 test-rmse:1.044134
#> [22] train-rmse:0.440875 test-rmse:1.034176
#> [23] train-rmse:0.436160 test-rmse:1.021020
#> [24] train-rmse:0.424089 test-rmse:1.019442
#> [25] train-rmse:0.415948 test-rmse:1.017073
#> [26] train-rmse:0.409145 test-rmse:1.010246
#> [27] train-rmse:0.403155 test-rmse:1.008974
#> [28] train-rmse:0.396269 test-rmse:1.001859
#> [29] train-rmse:0.388570 test-rmse:0.997404
#> [30] train-rmse:0.367946 test-rmse:0.988246
#> [31] train-rmse:0.355867 test-rmse:0.985757
#> [32] train-rmse:0.348772 test-rmse:0.978063
#> [33] train-rmse:0.344290 test-rmse:0.976274
#> [34] train-rmse:0.338605 test-rmse:0.973482
#> [35] train-rmse:0.332635 test-rmse:0.969367
#> [36] train-rmse:0.325480 test-rmse:0.968882
#> [37] train-rmse:0.311392 test-rmse:0.960815
#> [38] train-rmse:0.307876 test-rmse:0.957117
#> [39] train-rmse:0.301121 test-rmse:0.961341
#> [40] train-rmse:0.292149 test-rmse:0.960427
#> [41] train-rmse:0.282818 test-rmse:0.964506
#> [42] train-rmse:0.278837 test-rmse:0.963391
#> [43] train-rmse:0.268637 test-rmse:0.963308
#> [44] train-rmse:0.257547 test-rmse:0.966687
#> [45] train-rmse:0.250615 test-rmse:0.967755
#> [46] train-rmse:0.239646 test-rmse:0.967187
#> [47] train-rmse:0.237294 test-rmse:0.966015
#> [48] train-rmse:0.234782 test-rmse:0.965797
#> [49] train-rmse:0.231477 test-rmse:0.961542
#> [50] train-rmse:0.225015 test-rmse:0.963775
#> [51] train-rmse:0.219165 test-rmse:0.964368
#> [52] train-rmse:0.213663 test-rmse:0.964868
#> [53] train-rmse:0.208788 test-rmse:0.964958
#> [54] train-rmse:0.207119 test-rmse:0.965674
#> [55] train-rmse:0.203836 test-rmse:0.967984
#> [56] train-rmse:0.199716 test-rmse:0.966455
#> [57] train-rmse:0.193871 test-rmse:0.966643
#> [58] train-rmse:0.190569 test-rmse:0.963094
#> [59] train-rmse:0.184683 test-rmse:0.963604
#> [60] train-rmse:0.183089 test-rmse:0.963886
#> [61] train-rmse:0.176144 test-rmse:0.963669
#> [62] train-rmse:0.174598 test-rmse:0.963585
#> [63] train-rmse:0.168975 test-rmse:0.963066
#> [64] train-rmse:0.167391 test-rmse:0.962092
#> [65] train-rmse:0.163914 test-rmse:0.962890
#> [66] train-rmse:0.162084 test-rmse:0.964517
#> [67] train-rmse:0.161238 test-rmse:0.963340
#> [68] train-rmse:0.159824 test-rmse:0.961796
#> [69] train-rmse:0.155739 test-rmse:0.965794
#> [70] train-rmse:0.154449 test-rmse:0.965910
#> [1] train-rmse:27.621312 validation-rmse:28.745681
#> [2] train-rmse:19.650639 validation-rmse:20.615609
#> [3] train-rmse:13.990256 validation-rmse:14.668584
#> [4] train-rmse:9.993941 validation-rmse:10.420856
#> [5] train-rmse:7.154531 validation-rmse:7.479705
#> [6] train-rmse:5.145060 validation-rmse:5.416504
#> [7] train-rmse:3.721034 validation-rmse:3.900239
#> [8] train-rmse:2.710030 validation-rmse:2.873863
#> [9] train-rmse:2.000963 validation-rmse:2.137773
#> [10] train-rmse:1.507348 validation-rmse:1.607763
#> [11] train-rmse:1.168745 validation-rmse:1.276273
#> [12] train-rmse:0.941016 validation-rmse:1.054383
#> [13] train-rmse:0.787837 validation-rmse:0.904382
#> [14] train-rmse:0.679847 validation-rmse:0.827636
#> [15] train-rmse:0.612114 validation-rmse:0.782118
#> [16] train-rmse:0.558899 validation-rmse:0.747922
#> [17] train-rmse:0.528627 validation-rmse:0.710768
#> [18] train-rmse:0.508236 validation-rmse:0.700386
#> [19] train-rmse:0.479501 validation-rmse:0.697078
#> [20] train-rmse:0.463613 validation-rmse:0.692623
#> [21] train-rmse:0.447158 validation-rmse:0.696394
#> [22] train-rmse:0.440875 validation-rmse:0.692886
#> [23] train-rmse:0.436160 validation-rmse:0.691236
#> [24] train-rmse:0.424089 validation-rmse:0.695183
#> [25] train-rmse:0.415948 validation-rmse:0.694928
#> [26] train-rmse:0.409145 validation-rmse:0.695020
#> [27] train-rmse:0.403155 validation-rmse:0.689865
#> [28] train-rmse:0.396269 validation-rmse:0.696215
#> [29] train-rmse:0.388570 validation-rmse:0.692714
#> [30] train-rmse:0.367946 validation-rmse:0.678744
#> [31] train-rmse:0.355867 validation-rmse:0.675658
#> [32] train-rmse:0.348772 validation-rmse:0.675944
#> [33] train-rmse:0.344290 validation-rmse:0.674837
#> [34] train-rmse:0.338605 validation-rmse:0.671756
#> [35] train-rmse:0.332635 validation-rmse:0.667611
#> [36] train-rmse:0.325480 validation-rmse:0.672973
#> [37] train-rmse:0.311392 validation-rmse:0.657771
#> [38] train-rmse:0.307876 validation-rmse:0.655720
#> [39] train-rmse:0.301121 validation-rmse:0.657255
#> [40] train-rmse:0.292149 validation-rmse:0.647873
#> [41] train-rmse:0.282818 validation-rmse:0.647452
#> [42] train-rmse:0.278837 validation-rmse:0.643171
#> [43] train-rmse:0.268637 validation-rmse:0.644611
#> [44] train-rmse:0.257547 validation-rmse:0.643934
#> [45] train-rmse:0.250615 validation-rmse:0.641804
#> [46] train-rmse:0.239646 validation-rmse:0.638694
#> [47] train-rmse:0.237294 validation-rmse:0.640108
#> [48] train-rmse:0.234782 validation-rmse:0.643097
#> [49] train-rmse:0.231477 validation-rmse:0.639108
#> [50] train-rmse:0.225015 validation-rmse:0.638250
#> [51] train-rmse:0.219165 validation-rmse:0.635629
#> [52] train-rmse:0.213663 validation-rmse:0.636649
#> [53] train-rmse:0.208788 validation-rmse:0.638036
#> [54] train-rmse:0.207119 validation-rmse:0.638350
#> [55] train-rmse:0.203836 validation-rmse:0.639420
#> [56] train-rmse:0.199716 validation-rmse:0.636272
#> [57] train-rmse:0.193871 validation-rmse:0.635798
#> [58] train-rmse:0.190569 validation-rmse:0.635833
#> [59] train-rmse:0.184683 validation-rmse:0.636105
#> [60] train-rmse:0.183089 validation-rmse:0.637665
#> [61] train-rmse:0.176144 validation-rmse:0.640455
#> [62] train-rmse:0.174598 validation-rmse:0.639894
#> [63] train-rmse:0.168975 validation-rmse:0.639536
#> [64] train-rmse:0.167391 validation-rmse:0.639382
#> [65] train-rmse:0.163914 validation-rmse:0.638524
#> [66] train-rmse:0.162084 validation-rmse:0.639331
#> [67] train-rmse:0.161238 validation-rmse:0.639758
#> [68] train-rmse:0.159824 validation-rmse:0.641209
#> [69] train-rmse:0.155739 validation-rmse:0.643835
#> [70] train-rmse:0.154449 validation-rmse:0.644908
#> [1]
#> [1] "0.05 and 0.95 outliers for Cement, column number 1"
#> [1] "IQR = 157.625, 0.05 = 143.745 0.95 = 480"
#> [1] Cement Blast_Furnace_Slag Fly_Ash Water
#> [5] Superplasticizer Coarse_Aggregate Fine_Aggregate Age
#> [9] y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for Blast_Furnace_Slag, column number 2"
#> [1] "IQR = 142.95, 0.05 = 0 0.95 = 236"
#> [1] Cement Blast_Furnace_Slag Fly_Ash Water
#> [5] Superplasticizer Coarse_Aggregate Fine_Aggregate Age
#> [9] y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for Fly_Ash, column number 3"
#> [1] "IQR = 118.3, 0.05 = 0 0.95 = 167"
#> [1] Cement Blast_Furnace_Slag Fly_Ash Water
#> [5] Superplasticizer Coarse_Aggregate Fine_Aggregate Age
#> [9] y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for Water, column number 4"
#> [1] "IQR = 27.1, 0.05 = 146.1 0.95 = 228"
#> [1] Cement Blast_Furnace_Slag Fly_Ash Water
#> [5] Superplasticizer Coarse_Aggregate Fine_Aggregate Age
#> [9] y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for Superplasticizer, column number 5"
#> [1] "IQR = 10.2, 0.05 = 0 0.95 = 16.055"
#> Cement Blast_Furnace_Slag Fly_Ash Water Superplasticizer Coarse_Aggregate
#> 169 469 117.2 0 137.8 32.2 852.1
#> 123 469 117.2 0 137.8 32.2 852.1
#> 100 469 117.2 0 137.8 32.2 852.1
#> 77 469 117.2 0 137.8 32.2 852.1
#> 146 469 117.2 0 137.8 32.2 852.1
#> Fine_Aggregate Age y
#> 169 840.5 91 70.7
#> 123 840.5 28 66.9
#> 100 840.5 7 54.9
#> 77 840.5 3 40.2
#> 146 840.5 56 69.3
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for Coarse_Aggregate, column number 6"
#> [1] "IQR = 97.4000000000001, 0.05 = 842 0.95 = 1104"
#> [1] Cement Blast_Furnace_Slag Fly_Ash Water
#> [5] Superplasticizer Coarse_Aggregate Fine_Aggregate Age
#> [9] y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for Fine_Aggregate, column number 7"
#> [1] "IQR = 93.0500000000001, 0.05 = 613 0.95 = 898.09"
#> [1] Cement Blast_Furnace_Slag Fly_Ash Water
#> [5] Superplasticizer Coarse_Aggregate Fine_Aggregate Age
#> [9] y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for Age, column number 8"
#> [1] "IQR = 49, 0.05 = 3 0.95 = 180"
#> Cement Blast_Furnace_Slag Fly_Ash Water Superplasticizer Coarse_Aggregate
#> 799 500.0 0.0 0 200 0 1125.0
#> 67 139.6 209.4 0 192 0 1047.0
#> 770 331.0 0.0 0 192 0 978.0
#> 18 342.0 38.0 0 228 0 932.0
#> 26 380.0 0.0 0 228 0 932.0
#> 611 236.0 0.0 0 193 0 968.0
#> 815 310.0 0.0 0 192 0 970.0
#> 57 475.0 0.0 0 228 0 932.0
#> 32 266.0 114.0 0 228 0 932.0
#> 3 332.5 142.5 0 228 0 932.0
#> 25 380.0 0.0 0 228 0 932.0
#> 13 427.5 47.5 0 228 0 932.0
#> 64 190.0 190.0 0 228 0 932.0
#> 623 307.0 0.0 0 193 0 968.0
#> 36 237.5 237.5 0 228 0 932.0
#> 4 332.5 142.5 0 228 0 932.0
#> 31 304.0 76.0 0 228 0 932.0
#> 5 198.6 132.4 0 192 0 978.4
#> 35 190.0 190.0 0 228 0 932.0
#> 43 237.5 237.5 0 228 0 932.0
#> 617 277.0 0.0 0 191 0 968.0
#> 66 342.0 38.0 0 228 0 932.0
#> 27 380.0 95.0 0 228 0 932.0
#> 42 427.5 47.5 0 228 0 932.0
#> 621 254.0 0.0 0 198 0 968.0
#> 821 525.0 0.0 0 189 0 1125.0
#> 793 349.0 0.0 0 192 0 1047.0
#> 7 380.0 95.0 0 228 0 932.0
#> 61 304.0 76.0 0 228 0 932.0
#> 757 540.0 0.0 0 173 0 1125.0
#> 605 339.0 0.0 0 197 0 968.0
#> 34 475.0 0.0 0 228 0 932.0
#> 62 266.0 114.0 0 228 0 932.0
#> Fine_Aggregate Age y
#> 799 613.0 270 55.16
#> 67 806.9 360 44.70
#> 770 825.0 360 41.24
#> 18 670.0 365 56.14
#> 26 670.0 270 53.30
#> 611 885.0 365 25.08
#> 815 850.0 360 38.11
#> 57 594.0 365 41.93
#> 32 670.0 365 52.91
#> 3 594.0 270 40.27
#> 25 670.0 365 52.52
#> 13 594.0 270 43.01
#> 64 670.0 270 50.66
#> 623 812.0 365 36.15
#> 36 594.0 270 38.41
#> 4 594.0 365 41.05
#> 31 670.0 365 55.26
#> 5 825.5 360 44.30
#> 35 670.0 365 53.69
#> 43 594.0 365 39.00
#> 617 856.0 360 33.70
#> 66 670.0 270 55.06
#> 27 594.0 270 41.15
#> 42 594.0 365 43.70
#> 621 863.0 365 29.79
#> 821 613.0 270 67.11
#> 793 806.0 360 42.13
#> 7 594.0 365 43.70
#> 61 670.0 270 54.38
#> 757 613.0 270 74.17
#> 605 781.0 365 38.89
#> 34 594.0 270 42.13
#> 62 670.0 270 51.73
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for y, column number 9"
#> [1] "IQR = 22.425, 0.05 = 10.961 0.95 = 66.802"
#> [1] Cement Blast_Furnace_Slag Fly_Ash Water
#> [5] Superplasticizer Coarse_Aggregate Fine_Aggregate Age
#> [9] y
#> <0 rows> (or 0-length row.names)
#> [1]
#> $head_of_data
#>
#> $accuracy_plot
#>
#> $overfitting_plot
#>
#> $histograms
#>
#> $boxplots
#>
#> $predictor_vs_target
#>
#> $final_results_table
#>
#> $data_correlation
#>
#> $data_summary
#>
#> $head_of_ensemble
#>
#> $ensemble_correlation
#>
#> $accuracy_barchart
#>
#> $train_vs_holdout
#>
#> $duration_barchart
#>
#> $overfitting_barchart
#>
#> $bias_barchart
#>
#> $MSE_barchart
#>
#> $MAE_barchart
#>
#> $SSE_barchart
#>
#> $bias_plot
#>
#> $MSE_plot
#>
#> $MAE_plot
#>
#> $SSE_plot
#>
#> $colnum
#> [1] 9
#>
#> $numresamples
#> [1] 2
#>
#> $save_all_trained_modesl
#> [1] "N"
#>
#> $remove_ensemble_correlations_greater_than
#> [1] 1
#>
#> $train_amount
#> [1] 0.6
#>
#> $test_amount
#> [1] 0.2
#>
#> $validation_amount
#> [1] 0.2
#>
Numeric(data = Insurance,
colnum = 7,
numresamples = 2,
how_to_handle_strings = 1,
do_you_have_new_data = "N",
save_all_trained_models = "N",
remove_ensemble_correlations_greater_than = 1.00,
train_amount = 0.60,
test_amount = 0.20,
validation_amount = 0.20)
#> [1]
#> [1] "Resampling number 1 of 2,"
#> [1]
#> Number of parameters (weights and biases) to estimate: 18
#> Nguyen-Widrow method
#> Scaling factor= 0.7005963
#> gamma= 17.7991 alpha= 2.516 beta= 41758.61
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:13417.611562 test-rmse:11884.182714
#> [2] train-rmse:10022.239499 test-rmse:9015.516790
#> [3] train-rmse:7774.840027 test-rmse:7069.020388
#> [4] train-rmse:6358.520508 test-rmse:5882.003968
#> [5] train-rmse:5508.236119 test-rmse:5199.519524
#> [6] train-rmse:5006.537995 test-rmse:4803.929819
#> [7] train-rmse:4719.992006 test-rmse:4577.662788
#> [8] train-rmse:4559.660806 test-rmse:4468.729871
#> [9] train-rmse:4468.103109 test-rmse:4440.929630
#> [10] train-rmse:4408.711450 test-rmse:4390.289015
#> [11] train-rmse:4378.671306 test-rmse:4373.381760
#> [12] train-rmse:4339.295287 test-rmse:4380.011581
#> [13] train-rmse:4314.162701 test-rmse:4375.613971
#> [14] train-rmse:4278.115433 test-rmse:4361.855787
#> [15] train-rmse:4254.869908 test-rmse:4364.741404
#> [16] train-rmse:4221.821198 test-rmse:4358.468732
#> [17] train-rmse:4205.913225 test-rmse:4361.405038
#> [18] train-rmse:4188.252081 test-rmse:4380.221311
#> [19] train-rmse:4126.778378 test-rmse:4397.684621
#> [20] train-rmse:4090.072749 test-rmse:4407.454192
#> [21] train-rmse:4079.202682 test-rmse:4416.650236
#> [22] train-rmse:4062.414947 test-rmse:4420.892913
#> [23] train-rmse:4041.999683 test-rmse:4441.450825
#> [24] train-rmse:4033.591920 test-rmse:4436.054544
#> [25] train-rmse:4008.590513 test-rmse:4473.852247
#> [26] train-rmse:3992.910166 test-rmse:4479.556825
#> [27] train-rmse:3965.335111 test-rmse:4471.081082
#> [28] train-rmse:3954.472097 test-rmse:4471.775455
#> [29] train-rmse:3934.154417 test-rmse:4447.220987
#> [30] train-rmse:3926.105628 test-rmse:4447.863521
#> [31] train-rmse:3905.432519 test-rmse:4457.609335
#> [32] train-rmse:3886.592228 test-rmse:4460.696561
#> [33] train-rmse:3872.348341 test-rmse:4463.895113
#> [34] train-rmse:3865.158931 test-rmse:4471.656708
#> [35] train-rmse:3842.406466 test-rmse:4472.705023
#> [36] train-rmse:3823.537417 test-rmse:4466.972012
#> [37] train-rmse:3814.743728 test-rmse:4474.746160
#> [38] train-rmse:3798.026870 test-rmse:4500.944224
#> [39] train-rmse:3783.209432 test-rmse:4496.962494
#> [40] train-rmse:3767.795960 test-rmse:4504.937395
#> [41] train-rmse:3736.394206 test-rmse:4510.321837
#> [42] train-rmse:3729.787024 test-rmse:4513.091944
#> [43] train-rmse:3699.899544 test-rmse:4510.091305
#> [44] train-rmse:3685.372669 test-rmse:4492.401282
#> [45] train-rmse:3675.347523 test-rmse:4497.145864
#> [46] train-rmse:3646.371072 test-rmse:4499.173016
#> [47] train-rmse:3629.731846 test-rmse:4500.129067
#> [48] train-rmse:3609.465401 test-rmse:4504.328132
#> [49] train-rmse:3583.360979 test-rmse:4515.746697
#> [50] train-rmse:3574.531700 test-rmse:4527.842787
#> [51] train-rmse:3554.152870 test-rmse:4543.684378
#> [52] train-rmse:3544.886241 test-rmse:4557.155897
#> [53] train-rmse:3529.718802 test-rmse:4559.085046
#> [54] train-rmse:3524.432668 test-rmse:4553.483527
#> [55] train-rmse:3522.301674 test-rmse:4555.285308
#> [56] train-rmse:3499.952010 test-rmse:4558.147335
#> [57] train-rmse:3483.830587 test-rmse:4545.522841
#> [58] train-rmse:3472.969363 test-rmse:4551.644445
#> [59] train-rmse:3449.461905 test-rmse:4563.649121
#> [60] train-rmse:3443.261947 test-rmse:4568.417779
#> [61] train-rmse:3432.877501 test-rmse:4570.345394
#> [62] train-rmse:3412.557318 test-rmse:4580.292639
#> [63] train-rmse:3396.165946 test-rmse:4575.984225
#> [64] train-rmse:3385.776145 test-rmse:4581.239220
#> [65] train-rmse:3353.629777 test-rmse:4584.239765
#> [66] train-rmse:3331.733869 test-rmse:4587.148632
#> [67] train-rmse:3313.615715 test-rmse:4586.893385
#> [68] train-rmse:3309.040213 test-rmse:4589.772622
#> [69] train-rmse:3293.374573 test-rmse:4585.435212
#> [70] train-rmse:3276.515112 test-rmse:4583.916366
#> [1] train-rmse:13417.611562 validation-rmse:13224.624552
#> [2] train-rmse:10022.239499 validation-rmse:9847.571450
#> [3] train-rmse:7774.840027 validation-rmse:7596.959385
#> [4] train-rmse:6358.520508 validation-rmse:6179.119000
#> [5] train-rmse:5508.236119 validation-rmse:5320.448598
#> [6] train-rmse:5006.537995 validation-rmse:4846.824612
#> [7] train-rmse:4719.992006 validation-rmse:4558.723958
#> [8] train-rmse:4559.660806 validation-rmse:4405.609994
#> [9] train-rmse:4468.103109 validation-rmse:4353.510291
#> [10] train-rmse:4408.711450 validation-rmse:4281.568093
#> [11] train-rmse:4378.671306 validation-rmse:4253.763430
#> [12] train-rmse:4339.295287 validation-rmse:4259.091827
#> [13] train-rmse:4314.162701 validation-rmse:4248.192283
#> [14] train-rmse:4278.115433 validation-rmse:4227.390894
#> [15] train-rmse:4254.869908 validation-rmse:4224.228946
#> [16] train-rmse:4221.821198 validation-rmse:4242.233479
#> [17] train-rmse:4205.913225 validation-rmse:4240.934583
#> [18] train-rmse:4188.252081 validation-rmse:4237.694680
#> [19] train-rmse:4126.778378 validation-rmse:4255.164060
#> [20] train-rmse:4090.072749 validation-rmse:4251.033575
#> [21] train-rmse:4079.202682 validation-rmse:4245.561370
#> [22] train-rmse:4062.414947 validation-rmse:4250.798983
#> [23] train-rmse:4041.999683 validation-rmse:4257.333034
#> [24] train-rmse:4033.591920 validation-rmse:4259.853946
#> [25] train-rmse:4008.590513 validation-rmse:4263.531244
#> [26] train-rmse:3992.910166 validation-rmse:4264.545804
#> [27] train-rmse:3965.335111 validation-rmse:4291.810013
#> [28] train-rmse:3954.472097 validation-rmse:4291.113013
#> [29] train-rmse:3934.154417 validation-rmse:4291.497645
#> [30] train-rmse:3926.105628 validation-rmse:4289.953921
#> [31] train-rmse:3905.432519 validation-rmse:4299.733627
#> [32] train-rmse:3886.592228 validation-rmse:4301.892210
#> [33] train-rmse:3872.348341 validation-rmse:4320.461506
#> [34] train-rmse:3865.158931 validation-rmse:4325.510417
#> [35] train-rmse:3842.406466 validation-rmse:4326.537332
#> [36] train-rmse:3823.537417 validation-rmse:4356.573454
#> [37] train-rmse:3814.743728 validation-rmse:4377.149601
#> [38] train-rmse:3798.026870 validation-rmse:4380.521945
#> [39] train-rmse:3783.209432 validation-rmse:4405.651286
#> [40] train-rmse:3767.795960 validation-rmse:4414.561752
#> [41] train-rmse:3736.394206 validation-rmse:4417.602510
#> [42] train-rmse:3729.787024 validation-rmse:4420.964627
#> [43] train-rmse:3699.899544 validation-rmse:4431.713561
#> [44] train-rmse:3685.372669 validation-rmse:4456.343639
#> [45] train-rmse:3675.347523 validation-rmse:4453.084719
#> [46] train-rmse:3646.371072 validation-rmse:4451.833278
#> [47] train-rmse:3629.731846 validation-rmse:4453.850949
#> [48] train-rmse:3609.465401 validation-rmse:4456.964482
#> [49] train-rmse:3583.360979 validation-rmse:4467.750306
#> [50] train-rmse:3574.531700 validation-rmse:4469.407528
#> [51] train-rmse:3554.152870 validation-rmse:4487.869120
#> [52] train-rmse:3544.886241 validation-rmse:4480.082842
#> [53] train-rmse:3529.718802 validation-rmse:4478.613069
#> [54] train-rmse:3524.432668 validation-rmse:4477.693925
#> [55] train-rmse:3522.301674 validation-rmse:4481.020387
#> [56] train-rmse:3499.952010 validation-rmse:4513.292549
#> [57] train-rmse:3483.830587 validation-rmse:4513.331969
#> [58] train-rmse:3472.969363 validation-rmse:4514.103488
#> [59] train-rmse:3449.461905 validation-rmse:4516.644931
#> [60] train-rmse:3443.261947 validation-rmse:4536.696497
#> [61] train-rmse:3432.877501 validation-rmse:4542.247839
#> [62] train-rmse:3412.557318 validation-rmse:4562.965312
#> [63] train-rmse:3396.165946 validation-rmse:4568.572683
#> [64] train-rmse:3385.776145 validation-rmse:4574.974794
#> [65] train-rmse:3353.629777 validation-rmse:4582.140402
#> [66] train-rmse:3331.733869 validation-rmse:4580.264204
#> [67] train-rmse:3313.615715 validation-rmse:4591.354638
#> [68] train-rmse:3309.040213 validation-rmse:4592.874745
#> [69] train-rmse:3293.374573 validation-rmse:4593.361955
#> [70] train-rmse:3276.515112 validation-rmse:4600.692743
#> [1]
#> [1] "Working on the Ensembles section"
#> [1]
#> Number of parameters (weights and biases) to estimate: 54
#> Nguyen-Widrow method
#> Scaling factor= 0.7015519
#> gamma= 33.0998 alpha= 6.1001 beta= 16047.64
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:11676.716089 test-rmse:14335.384317
#> [2] train-rmse:8354.095279 test-rmse:10553.029297
#> [3] train-rmse:5989.394793 test-rmse:7924.410920
#> [4] train-rmse:4310.489824 test-rmse:6087.028331
#> [5] train-rmse:3109.404834 test-rmse:4777.556680
#> [6] train-rmse:2250.237844 test-rmse:3865.883116
#> [7] train-rmse:1640.817172 test-rmse:3231.758979
#> [8] train-rmse:1209.100401 test-rmse:2816.527459
#> [9] train-rmse:905.989475 test-rmse:2538.832621
#> [10] train-rmse:691.590253 test-rmse:2335.663299
#> [11] train-rmse:542.013279 test-rmse:2186.264749
#> [12] train-rmse:440.193932 test-rmse:2091.654558
#> [13] train-rmse:372.662046 test-rmse:2012.557950
#> [14] train-rmse:328.422724 test-rmse:1955.101452
#> [15] train-rmse:298.196321 test-rmse:1913.263979
#> [16] train-rmse:279.537645 test-rmse:1882.413140
#> [17] train-rmse:265.796462 test-rmse:1859.623964
#> [18] train-rmse:249.648340 test-rmse:1853.134091
#> [19] train-rmse:241.843262 test-rmse:1837.120771
#> [20] train-rmse:234.078816 test-rmse:1825.105837
#> [21] train-rmse:227.312846 test-rmse:1810.797398
#> [22] train-rmse:222.191300 test-rmse:1804.764481
#> [23] train-rmse:212.213766 test-rmse:1801.559191
#> [24] train-rmse:210.796199 test-rmse:1792.776360
#> [25] train-rmse:205.543606 test-rmse:1792.293552
#> [26] train-rmse:202.053346 test-rmse:1790.398641
#> [27] train-rmse:192.833947 test-rmse:1790.204722
#> [28] train-rmse:184.847917 test-rmse:1789.228178
#> [29] train-rmse:182.264282 test-rmse:1782.947741
#> [30] train-rmse:177.181996 test-rmse:1782.530513
#> [31] train-rmse:174.610467 test-rmse:1780.739959
#> [32] train-rmse:167.466793 test-rmse:1779.946488
#> [33] train-rmse:163.238027 test-rmse:1780.239539
#> [34] train-rmse:159.410908 test-rmse:1775.406309
#> [35] train-rmse:157.277497 test-rmse:1775.334236
#> [36] train-rmse:155.447400 test-rmse:1774.776873
#> [37] train-rmse:152.578687 test-rmse:1770.876848
#> [38] train-rmse:148.772578 test-rmse:1770.620438
#> [39] train-rmse:145.448460 test-rmse:1770.615717
#> [40] train-rmse:141.919898 test-rmse:1771.403732
#> [41] train-rmse:140.023894 test-rmse:1768.178922
#> [42] train-rmse:138.835579 test-rmse:1768.250647
#> [43] train-rmse:134.715782 test-rmse:1767.727257
#> [44] train-rmse:133.708627 test-rmse:1767.712262
#> [45] train-rmse:131.359896 test-rmse:1767.348169
#> [46] train-rmse:129.315551 test-rmse:1767.271794
#> [47] train-rmse:126.663079 test-rmse:1768.336564
#> [48] train-rmse:123.449975 test-rmse:1771.506021
#> [49] train-rmse:120.723515 test-rmse:1771.914632
#> [50] train-rmse:116.987646 test-rmse:1770.957913
#> [51] train-rmse:113.675563 test-rmse:1771.285163
#> [52] train-rmse:110.302305 test-rmse:1770.684697
#> [53] train-rmse:105.967421 test-rmse:1769.712296
#> [54] train-rmse:105.289869 test-rmse:1766.608436
#> [55] train-rmse:103.774474 test-rmse:1766.512894
#> [56] train-rmse:102.962973 test-rmse:1766.418135
#> [57] train-rmse:101.246207 test-rmse:1766.326857
#> [58] train-rmse:98.388259 test-rmse:1764.834174
#> [59] train-rmse:97.341114 test-rmse:1762.232105
#> [60] train-rmse:96.001068 test-rmse:1762.154763
#> [61] train-rmse:94.205784 test-rmse:1764.150357
#> [62] train-rmse:91.573428 test-rmse:1763.702795
#> [63] train-rmse:90.405158 test-rmse:1762.396145
#> [64] train-rmse:88.622261 test-rmse:1765.798959
#> [65] train-rmse:87.746766 test-rmse:1767.618248
#> [66] train-rmse:86.507148 test-rmse:1767.415449
#> [67] train-rmse:84.493733 test-rmse:1767.488290
#> [68] train-rmse:83.240083 test-rmse:1768.443047
#> [69] train-rmse:82.828157 test-rmse:1768.411466
#> [70] train-rmse:81.529083 test-rmse:1768.503565
#> [1] train-rmse:11676.716089 validation-rmse:12113.270065
#> [2] train-rmse:8354.095279 validation-rmse:8696.271124
#> [3] train-rmse:5989.394793 validation-rmse:6260.429097
#> [4] train-rmse:4310.489824 validation-rmse:4628.441962
#> [5] train-rmse:3109.404834 validation-rmse:3435.271189
#> [6] train-rmse:2250.237844 validation-rmse:2615.939332
#> [7] train-rmse:1640.817172 validation-rmse:2006.106114
#> [8] train-rmse:1209.100401 validation-rmse:1579.503857
#> [9] train-rmse:905.989475 validation-rmse:1283.353996
#> [10] train-rmse:691.590253 validation-rmse:1067.988327
#> [11] train-rmse:542.013279 validation-rmse:919.002767
#> [12] train-rmse:440.193932 validation-rmse:823.515971
#> [13] train-rmse:372.662046 validation-rmse:748.933995
#> [14] train-rmse:328.422724 validation-rmse:694.985378
#> [15] train-rmse:298.196321 validation-rmse:657.274858
#> [16] train-rmse:279.537645 validation-rmse:628.986743
#> [17] train-rmse:265.796462 validation-rmse:607.863714
#> [18] train-rmse:249.648340 validation-rmse:597.981856
#> [19] train-rmse:241.843262 validation-rmse:586.190119
#> [20] train-rmse:234.078816 validation-rmse:572.690798
#> [21] train-rmse:227.312846 validation-rmse:562.649213
#> [22] train-rmse:222.191300 validation-rmse:557.378325
#> [23] train-rmse:212.213766 validation-rmse:549.073475
#> [24] train-rmse:210.796199 validation-rmse:542.009897
#> [25] train-rmse:205.543606 validation-rmse:540.937959
#> [26] train-rmse:202.053346 validation-rmse:539.290564
#> [27] train-rmse:192.833947 validation-rmse:537.068297
#> [28] train-rmse:184.847917 validation-rmse:536.236451
#> [29] train-rmse:182.264282 validation-rmse:532.223915
#> [30] train-rmse:177.181996 validation-rmse:530.345016
#> [31] train-rmse:174.610467 validation-rmse:530.776009
#> [32] train-rmse:167.466793 validation-rmse:529.077005
#> [33] train-rmse:163.238027 validation-rmse:528.514103
#> [34] train-rmse:159.410908 validation-rmse:524.941065
#> [35] train-rmse:157.277497 validation-rmse:525.993721
#> [36] train-rmse:155.447400 validation-rmse:524.960444
#> [37] train-rmse:152.578687 validation-rmse:519.616286
#> [38] train-rmse:148.772578 validation-rmse:518.542838
#> [39] train-rmse:145.448460 validation-rmse:519.035313
#> [40] train-rmse:141.919898 validation-rmse:521.111818
#> [41] train-rmse:140.023894 validation-rmse:516.634528
#> [42] train-rmse:138.835579 validation-rmse:516.414941
#> [43] train-rmse:134.715782 validation-rmse:515.710450
#> [44] train-rmse:133.708627 validation-rmse:515.613351
#> [45] train-rmse:131.359896 validation-rmse:515.080537
#> [46] train-rmse:129.315551 validation-rmse:515.760335
#> [47] train-rmse:126.663079 validation-rmse:516.653327
#> [48] train-rmse:123.449975 validation-rmse:521.002099
#> [49] train-rmse:120.723515 validation-rmse:519.782429
#> [50] train-rmse:116.987646 validation-rmse:518.091430
#> [51] train-rmse:113.675563 validation-rmse:520.783069
#> [52] train-rmse:110.302305 validation-rmse:520.323780
#> [53] train-rmse:105.967421 validation-rmse:520.749124
#> [54] train-rmse:105.289869 validation-rmse:518.347020
#> [55] train-rmse:103.774474 validation-rmse:518.433615
#> [56] train-rmse:102.962973 validation-rmse:518.234254
#> [57] train-rmse:101.246207 validation-rmse:517.919822
#> [58] train-rmse:98.388259 validation-rmse:512.356774
#> [59] train-rmse:97.341114 validation-rmse:508.404032
#> [60] train-rmse:96.001068 validation-rmse:508.033100
#> [61] train-rmse:94.205784 validation-rmse:511.192710
#> [62] train-rmse:91.573428 validation-rmse:510.310022
#> [63] train-rmse:90.405158 validation-rmse:506.417421
#> [64] train-rmse:88.622261 validation-rmse:507.447312
#> [65] train-rmse:87.746766 validation-rmse:505.937490
#> [66] train-rmse:86.507148 validation-rmse:505.244917
#> [67] train-rmse:84.493733 validation-rmse:506.198471
#> [68] train-rmse:83.240083 validation-rmse:506.956349
#> [69] train-rmse:82.828157 validation-rmse:507.031100
#> [70] train-rmse:81.529083 validation-rmse:507.084332
#> [1]
#> [1] "Resampling number 2 of 2,"
#> [1]
#> Number of parameters (weights and biases) to estimate: 18
#> Nguyen-Widrow method
#> Scaling factor= 0.700606
#> gamma= 17.7011 alpha= 2.4823 beta= 40898.3
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:13091.289080 test-rmse:13630.503919
#> [2] train-rmse:9808.091032 test-rmse:10344.663415
#> [3] train-rmse:7638.152950 test-rmse:8206.741234
#> [4] train-rmse:6271.808237 test-rmse:6899.227719
#> [5] train-rmse:5440.278268 test-rmse:6093.113194
#> [6] train-rmse:4958.582646 test-rmse:5643.977695
#> [7] train-rmse:4671.873405 test-rmse:5361.682750
#> [8] train-rmse:4510.961951 test-rmse:5225.410893
#> [9] train-rmse:4407.075416 test-rmse:5166.147031
#> [10] train-rmse:4333.013751 test-rmse:5137.778770
#> [11] train-rmse:4274.336260 test-rmse:5109.604335
#> [12] train-rmse:4241.165284 test-rmse:5099.030387
#> [13] train-rmse:4179.703253 test-rmse:5110.298374
#> [14] train-rmse:4158.087222 test-rmse:5108.541624
#> [15] train-rmse:4108.246083 test-rmse:5122.163865
#> [16] train-rmse:4054.580221 test-rmse:5116.024031
#> [17] train-rmse:4033.934735 test-rmse:5114.840698
#> [18] train-rmse:4009.152807 test-rmse:5119.583826
#> [19] train-rmse:3994.714299 test-rmse:5131.453185
#> [20] train-rmse:3965.216102 test-rmse:5130.261150
#> [21] train-rmse:3929.727757 test-rmse:5130.376266
#> [22] train-rmse:3899.849011 test-rmse:5148.412747
#> [23] train-rmse:3863.210544 test-rmse:5156.143323
#> [24] train-rmse:3833.324067 test-rmse:5159.078984
#> [25] train-rmse:3808.319000 test-rmse:5159.406137
#> [26] train-rmse:3802.004006 test-rmse:5157.904244
#> [27] train-rmse:3790.773068 test-rmse:5167.781086
#> [28] train-rmse:3769.378641 test-rmse:5174.225592
#> [29] train-rmse:3743.220983 test-rmse:5175.681581
#> [30] train-rmse:3735.051610 test-rmse:5174.663357
#> [31] train-rmse:3707.568300 test-rmse:5179.279715
#> [32] train-rmse:3699.487539 test-rmse:5189.911333
#> [33] train-rmse:3684.718906 test-rmse:5190.955718
#> [34] train-rmse:3670.892633 test-rmse:5200.245327
#> [35] train-rmse:3647.968412 test-rmse:5199.936194
#> [36] train-rmse:3629.423091 test-rmse:5227.782163
#> [37] train-rmse:3609.326472 test-rmse:5242.205034
#> [38] train-rmse:3599.731539 test-rmse:5258.550981
#> [39] train-rmse:3582.300571 test-rmse:5275.640585
#> [40] train-rmse:3576.960501 test-rmse:5274.845607
#> [41] train-rmse:3566.560989 test-rmse:5286.814438
#> [42] train-rmse:3544.631542 test-rmse:5288.739355
#> [43] train-rmse:3511.323302 test-rmse:5298.553097
#> [44] train-rmse:3488.788131 test-rmse:5295.640711
#> [45] train-rmse:3470.892692 test-rmse:5302.095740
#> [46] train-rmse:3468.460291 test-rmse:5302.163443
#> [47] train-rmse:3446.352161 test-rmse:5296.598495
#> [48] train-rmse:3442.269105 test-rmse:5296.162547
#> [49] train-rmse:3423.783246 test-rmse:5300.195450
#> [50] train-rmse:3397.813321 test-rmse:5306.316790
#> [51] train-rmse:3390.091228 test-rmse:5311.962685
#> [52] train-rmse:3384.326612 test-rmse:5316.354518
#> [53] train-rmse:3381.318165 test-rmse:5314.323833
#> [54] train-rmse:3361.804524 test-rmse:5316.939774
#> [55] train-rmse:3353.521697 test-rmse:5325.831293
#> [56] train-rmse:3331.197663 test-rmse:5324.384677
#> [57] train-rmse:3314.456390 test-rmse:5322.747836
#> [58] train-rmse:3302.858412 test-rmse:5332.477679
#> [59] train-rmse:3288.118895 test-rmse:5341.693911
#> [60] train-rmse:3285.743962 test-rmse:5341.174745
#> [61] train-rmse:3272.264118 test-rmse:5349.408116
#> [62] train-rmse:3258.429782 test-rmse:5355.972682
#> [63] train-rmse:3239.456997 test-rmse:5363.485660
#> [64] train-rmse:3222.385409 test-rmse:5374.242503
#> [65] train-rmse:3203.538543 test-rmse:5370.719510
#> [66] train-rmse:3190.452087 test-rmse:5375.052057
#> [67] train-rmse:3179.422155 test-rmse:5386.937062
#> [68] train-rmse:3171.117726 test-rmse:5392.472189
#> [69] train-rmse:3151.548317 test-rmse:5400.540885
#> [70] train-rmse:3148.761000 test-rmse:5401.073211
#> [1] train-rmse:13091.289080 validation-rmse:12457.446551
#> [2] train-rmse:9808.091032 validation-rmse:9038.797199
#> [3] train-rmse:7638.152950 validation-rmse:6753.584515
#> [4] train-rmse:6271.808237 validation-rmse:5290.812861
#> [5] train-rmse:5440.278268 validation-rmse:4388.524901
#> [6] train-rmse:4958.582646 validation-rmse:3905.637460
#> [7] train-rmse:4671.873405 validation-rmse:3636.416234
#> [8] train-rmse:4510.961951 validation-rmse:3555.576689
#> [9] train-rmse:4407.075416 validation-rmse:3508.388911
#> [10] train-rmse:4333.013751 validation-rmse:3494.811886
#> [11] train-rmse:4274.336260 validation-rmse:3500.076369
#> [12] train-rmse:4241.165284 validation-rmse:3478.591434
#> [13] train-rmse:4179.703253 validation-rmse:3506.376045
#> [14] train-rmse:4158.087222 validation-rmse:3504.944458
#> [15] train-rmse:4108.246083 validation-rmse:3506.656332
#> [16] train-rmse:4054.580221 validation-rmse:3494.051740
#> [17] train-rmse:4033.934735 validation-rmse:3495.680095
#> [18] train-rmse:4009.152807 validation-rmse:3517.582259
#> [19] train-rmse:3994.714299 validation-rmse:3514.855017
#> [20] train-rmse:3965.216102 validation-rmse:3520.080901
#> [21] train-rmse:3929.727757 validation-rmse:3522.362626
#> [22] train-rmse:3899.849011 validation-rmse:3518.369417
#> [23] train-rmse:3863.210544 validation-rmse:3517.637396
#> [24] train-rmse:3833.324067 validation-rmse:3524.609897
#> [25] train-rmse:3808.319000 validation-rmse:3558.580507
#> [26] train-rmse:3802.004006 validation-rmse:3560.385375
#> [27] train-rmse:3790.773068 validation-rmse:3564.716215
#> [28] train-rmse:3769.378641 validation-rmse:3593.372386
#> [29] train-rmse:3743.220983 validation-rmse:3625.992174
#> [30] train-rmse:3735.051610 validation-rmse:3624.836884
#> [31] train-rmse:3707.568300 validation-rmse:3624.893930
#> [32] train-rmse:3699.487539 validation-rmse:3629.068284
#> [33] train-rmse:3684.718906 validation-rmse:3633.289993
#> [34] train-rmse:3670.892633 validation-rmse:3634.266711
#> [35] train-rmse:3647.968412 validation-rmse:3624.068876
#> [36] train-rmse:3629.423091 validation-rmse:3630.525040
#> [37] train-rmse:3609.326472 validation-rmse:3660.669123
#> [38] train-rmse:3599.731539 validation-rmse:3659.725631
#> [39] train-rmse:3582.300571 validation-rmse:3662.490896
#> [40] train-rmse:3576.960501 validation-rmse:3665.939504
#> [41] train-rmse:3566.560989 validation-rmse:3677.067603
#> [42] train-rmse:3544.631542 validation-rmse:3686.459232
#> [43] train-rmse:3511.323302 validation-rmse:3695.704252
#> [44] train-rmse:3488.788131 validation-rmse:3690.438384
#> [45] train-rmse:3470.892692 validation-rmse:3693.400884
#> [46] train-rmse:3468.460291 validation-rmse:3693.740255
#> [47] train-rmse:3446.352161 validation-rmse:3708.612585
#> [48] train-rmse:3442.269105 validation-rmse:3710.635073
#> [49] train-rmse:3423.783246 validation-rmse:3725.372095
#> [50] train-rmse:3397.813321 validation-rmse:3728.421904
#> [51] train-rmse:3390.091228 validation-rmse:3727.420448
#> [52] train-rmse:3384.326612 validation-rmse:3732.710226
#> [53] train-rmse:3381.318165 validation-rmse:3731.317976
#> [54] train-rmse:3361.804524 validation-rmse:3732.625861
#> [55] train-rmse:3353.521697 validation-rmse:3729.344454
#> [56] train-rmse:3331.197663 validation-rmse:3733.489591
#> [57] train-rmse:3314.456390 validation-rmse:3733.012205
#> [58] train-rmse:3302.858412 validation-rmse:3734.186624
#> [59] train-rmse:3288.118895 validation-rmse:3744.187566
#> [60] train-rmse:3285.743962 validation-rmse:3744.330255
#> [61] train-rmse:3272.264118 validation-rmse:3759.310329
#> [62] train-rmse:3258.429782 validation-rmse:3765.135142
#> [63] train-rmse:3239.456997 validation-rmse:3780.107207
#> [64] train-rmse:3222.385409 validation-rmse:3795.026810
#> [65] train-rmse:3203.538543 validation-rmse:3794.253293
#> [66] train-rmse:3190.452087 validation-rmse:3796.907675
#> [67] train-rmse:3179.422155 validation-rmse:3804.137027
#> [68] train-rmse:3171.117726 validation-rmse:3807.449074
#> [69] train-rmse:3151.548317 validation-rmse:3801.748059
#> [70] train-rmse:3148.761000 validation-rmse:3802.394612
#> [1]
#> [1] "Working on the Ensembles section"
#> [1]
#> Number of parameters (weights and biases) to estimate: 54
#> Nguyen-Widrow method
#> Scaling factor= 0.7014119
#> gamma= 34.1775 alpha= 8.1561 beta= 16572.21
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> Using 100 trees...
#> [1] train-rmse:12724.052969 test-rmse:14640.924241
#> [2] train-rmse:9116.585933 test-rmse:10546.379808
#> [3] train-rmse:6529.850289 test-rmse:7564.627689
#> [4] train-rmse:4702.598805 test-rmse:5412.191719
#> [5] train-rmse:3401.435874 test-rmse:3870.122476
#> [6] train-rmse:2472.108286 test-rmse:2760.504988
#> [7] train-rmse:1815.356562 test-rmse:1956.408483
#> [8] train-rmse:1355.625334 test-rmse:1431.313264
#> [9] train-rmse:1035.794014 test-rmse:1063.434894
#> [10] train-rmse:811.276038 test-rmse:823.445392
#> [11] train-rmse:652.925493 test-rmse:665.740591
#> [12] train-rmse:543.384145 test-rmse:577.451751
#> [13] train-rmse:470.800939 test-rmse:514.079310
#> [14] train-rmse:418.956563 test-rmse:481.932315
#> [15] train-rmse:383.445948 test-rmse:466.146242
#> [16] train-rmse:357.261637 test-rmse:451.786768
#> [17] train-rmse:335.656047 test-rmse:454.429125
#> [18] train-rmse:322.365753 test-rmse:450.051414
#> [19] train-rmse:310.584327 test-rmse:444.676679
#> [20] train-rmse:301.509125 test-rmse:440.389156
#> [21] train-rmse:289.067172 test-rmse:443.403698
#> [22] train-rmse:281.810900 test-rmse:442.472360
#> [23] train-rmse:278.260614 test-rmse:441.138512
#> [24] train-rmse:275.446320 test-rmse:440.451245
#> [25] train-rmse:273.358112 test-rmse:440.758948
#> [26] train-rmse:268.716735 test-rmse:437.983261
#> [27] train-rmse:263.819563 test-rmse:441.979749
#> [28] train-rmse:255.069287 test-rmse:442.507715
#> [29] train-rmse:247.663007 test-rmse:447.037976
#> [30] train-rmse:240.255951 test-rmse:443.359188
#> [31] train-rmse:235.826586 test-rmse:445.509123
#> [32] train-rmse:229.610376 test-rmse:444.769793
#> [33] train-rmse:222.298984 test-rmse:440.678699
#> [34] train-rmse:216.174118 test-rmse:433.669890
#> [35] train-rmse:212.831934 test-rmse:433.861987
#> [36] train-rmse:209.626321 test-rmse:432.733279
#> [37] train-rmse:203.864102 test-rmse:432.280044
#> [38] train-rmse:195.816531 test-rmse:428.378747
#> [39] train-rmse:190.000485 test-rmse:429.753185
#> [40] train-rmse:187.894802 test-rmse:429.957351
#> [41] train-rmse:183.969858 test-rmse:430.278355
#> [42] train-rmse:180.523092 test-rmse:428.815031
#> [43] train-rmse:178.391632 test-rmse:428.781370
#> [44] train-rmse:172.772266 test-rmse:425.565173
#> [45] train-rmse:170.627432 test-rmse:424.120128
#> [46] train-rmse:165.422348 test-rmse:417.200456
#> [47] train-rmse:160.149285 test-rmse:417.709738
#> [48] train-rmse:157.189350 test-rmse:416.230850
#> [49] train-rmse:154.534444 test-rmse:417.394454
#> [50] train-rmse:153.278806 test-rmse:416.659392
#> [51] train-rmse:149.191984 test-rmse:414.706460
#> [52] train-rmse:146.840712 test-rmse:413.702198
#> [53] train-rmse:145.331981 test-rmse:414.176287
#> [54] train-rmse:142.234330 test-rmse:411.631257
#> [55] train-rmse:140.538872 test-rmse:412.002827
#> [56] train-rmse:139.201399 test-rmse:411.476135
#> [57] train-rmse:137.785678 test-rmse:410.661655
#> [58] train-rmse:135.971646 test-rmse:409.224341
#> [59] train-rmse:134.686016 test-rmse:407.637654
#> [60] train-rmse:130.880705 test-rmse:405.182359
#> [61] train-rmse:126.538542 test-rmse:405.450475
#> [62] train-rmse:124.015668 test-rmse:402.730183
#> [63] train-rmse:123.132362 test-rmse:402.710735
#> [64] train-rmse:119.162464 test-rmse:404.637977
#> [65] train-rmse:116.686496 test-rmse:401.521048
#> [66] train-rmse:113.962333 test-rmse:402.284958
#> [67] train-rmse:112.316272 test-rmse:401.461360
#> [68] train-rmse:108.790818 test-rmse:399.260551
#> [69] train-rmse:105.822807 test-rmse:400.357967
#> [70] train-rmse:102.623468 test-rmse:397.184806
#> [1] train-rmse:12724.052969 validation-rmse:11400.337843
#> [2] train-rmse:9116.585933 validation-rmse:7834.587699
#> [3] train-rmse:6529.850289 validation-rmse:5474.873860
#> [4] train-rmse:4702.598805 validation-rmse:3953.845723
#> [5] train-rmse:3401.435874 validation-rmse:2809.751468
#> [6] train-rmse:2472.108286 validation-rmse:2015.515345
#> [7] train-rmse:1815.356562 validation-rmse:1455.988851
#> [8] train-rmse:1355.625334 validation-rmse:1090.837307
#> [9] train-rmse:1035.794014 validation-rmse:841.283415
#> [10] train-rmse:811.276038 validation-rmse:673.595426
#> [11] train-rmse:652.925493 validation-rmse:574.501661
#> [12] train-rmse:543.384145 validation-rmse:516.249070
#> [13] train-rmse:470.800939 validation-rmse:485.501170
#> [14] train-rmse:418.956563 validation-rmse:478.379636
#> [15] train-rmse:383.445948 validation-rmse:476.521543
#> [16] train-rmse:357.261637 validation-rmse:471.058626
#> [17] train-rmse:335.656047 validation-rmse:468.855300
#> [18] train-rmse:322.365753 validation-rmse:466.749389
#> [19] train-rmse:310.584327 validation-rmse:464.924841
#> [20] train-rmse:301.509125 validation-rmse:462.223585
#> [21] train-rmse:289.067172 validation-rmse:460.628258
#> [22] train-rmse:281.810900 validation-rmse:461.623640
#> [23] train-rmse:278.260614 validation-rmse:461.163535
#> [24] train-rmse:275.446320 validation-rmse:459.103782
#> [25] train-rmse:273.358112 validation-rmse:459.263915
#> [26] train-rmse:268.716735 validation-rmse:457.391954
#> [27] train-rmse:263.819563 validation-rmse:456.875335
#> [28] train-rmse:255.069287 validation-rmse:458.686903
#> [29] train-rmse:247.663007 validation-rmse:451.417507
#> [30] train-rmse:240.255951 validation-rmse:451.718172
#> [31] train-rmse:235.826586 validation-rmse:451.653052
#> [32] train-rmse:229.610376 validation-rmse:452.167795
#> [33] train-rmse:222.298984 validation-rmse:456.432236
#> [34] train-rmse:216.174118 validation-rmse:452.849371
#> [35] train-rmse:212.831934 validation-rmse:451.098592
#> [36] train-rmse:209.626321 validation-rmse:449.759982
#> [37] train-rmse:203.864102 validation-rmse:450.385413
#> [38] train-rmse:195.816531 validation-rmse:449.949209
#> [39] train-rmse:190.000485 validation-rmse:450.198795
#> [40] train-rmse:187.894802 validation-rmse:450.878040
#> [41] train-rmse:183.969858 validation-rmse:451.374028
#> [42] train-rmse:180.523092 validation-rmse:453.812628
#> [43] train-rmse:178.391632 validation-rmse:452.516319
#> [44] train-rmse:172.772266 validation-rmse:452.371676
#> [45] train-rmse:170.627432 validation-rmse:452.558629
#> [46] train-rmse:165.422348 validation-rmse:449.206459
#> [47] train-rmse:160.149285 validation-rmse:452.407930
#> [48] train-rmse:157.189350 validation-rmse:456.019889
#> [49] train-rmse:154.534444 validation-rmse:457.472426
#> [50] train-rmse:153.278806 validation-rmse:456.394221
#> [51] train-rmse:149.191984 validation-rmse:454.963080
#> [52] train-rmse:146.840712 validation-rmse:455.116413
#> [53] train-rmse:145.331981 validation-rmse:456.696645
#> [54] train-rmse:142.234330 validation-rmse:456.481966
#> [55] train-rmse:140.538872 validation-rmse:455.651668
#> [56] train-rmse:139.201399 validation-rmse:457.108435
#> [57] train-rmse:137.785678 validation-rmse:456.691594
#> [58] train-rmse:135.971646 validation-rmse:458.247285
#> [59] train-rmse:134.686016 validation-rmse:458.535375
#> [60] train-rmse:130.880705 validation-rmse:459.310124
#> [61] train-rmse:126.538542 validation-rmse:456.971244
#> [62] train-rmse:124.015668 validation-rmse:456.975845
#> [63] train-rmse:123.132362 validation-rmse:456.301918
#> [64] train-rmse:119.162464 validation-rmse:454.667804
#> [65] train-rmse:116.686496 validation-rmse:456.081477
#> [66] train-rmse:113.962333 validation-rmse:455.722177
#> [67] train-rmse:112.316272 validation-rmse:455.044131
#> [68] train-rmse:108.790818 validation-rmse:451.944869
#> [69] train-rmse:105.822807 validation-rmse:451.663539
#> [70] train-rmse:102.623468 validation-rmse:450.908313
#> [1]
#> [1] "0.05 and 0.95 outliers for age, column number 1"
#> [1] "IQR = 24, 0.05 = 18 0.95 = 62"
#> [1] age sex bmi children smoker region y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for sex, column number 2"
#> [1] "IQR = 1, 0.05 = 1 0.95 = 2"
#> [1] age sex bmi children smoker region y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for bmi, column number 3"
#> [1] "IQR = 8.3975, 0.05 = 21.256 0.95 = 41.106"
#> [1] age sex bmi children smoker region y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for children, column number 4"
#> [1] "IQR = 2, 0.05 = 0 0.95 = 3"
#> [1] age sex bmi children smoker region y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for smoker, column number 5"
#> [1] "IQR = 0, 0.05 = 1 0.95 = 2"
#> [1] age sex bmi children smoker region y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for region, column number 6"
#> [1] "IQR = 1, 0.05 = 1 0.95 = 4"
#> [1] age sex bmi children smoker region y
#> <0 rows> (or 0-length row.names)
#> [1]
#> [1]
#> [1] "0.05 and 0.95 outliers for y, column number 7"
#> [1] "IQR = 11899.625365, 0.05 = 1757.7534 0.95 = 41181.8277874999"
#> age sex bmi children smoker region y
#> 544 54 1 47.410 0 2 3 63770.43
#> 1231 52 2 34.485 3 2 2 60021.40
#> 1301 45 2 30.360 0 2 3 62592.87
#> [1]
#> $head_of_data
#>
#> $accuracy_plot
#>
#> $overfitting_plot
#>
#> $histograms
#>
#> $boxplots
#>
#> $predictor_vs_target
#>
#> $final_results_table
#>
#> $data_correlation
#>
#> $data_summary
#>
#> $head_of_ensemble
#>
#> $ensemble_correlation
#>
#> $accuracy_barchart
#>
#> $train_vs_holdout
#>
#> $duration_barchart
#>
#> $overfitting_barchart
#>
#> $bias_barchart
#>
#> $MSE_barchart
#>
#> $MAE_barchart
#>
#> $SSE_barchart
#>
#> $bias_plot
#>
#> $MSE_plot
#>
#> $MAE_plot
#>
#> $SSE_plot
#>
#> $colnum
#> [1] 7
#>
#> $numresamples
#> [1] 2
#>
#> $save_all_trained_modesl
#> [1] "N"
#>
#> $remove_ensemble_correlations_greater_than
#> [1] 1
#>
#> $train_amount
#> [1] 0.6
#>
#> $test_amount
#> [1] 0.2
#>
#> $validation_amount
#> [1] 0.2
#>