Exploratory Data Analysis and PreProcessing
## [1] 2571 33
## [1] 267 33
## [1] 2038
## Brand Code Carb Volume Fill Ounces PC Volume
## Length:2571 Min. :5.040 Min. :23.63 Min. :0.07933
## Class :character 1st Qu.:5.293 1st Qu.:23.92 1st Qu.:0.23917
## Mode :character Median :5.347 Median :23.97 Median :0.27133
## Mean :5.370 Mean :23.97 Mean :0.27712
## 3rd Qu.:5.453 3rd Qu.:24.03 3rd Qu.:0.31200
## Max. :5.700 Max. :24.32 Max. :0.47800
## NA's :10 NA's :38 NA's :39
## Carb Pressure Carb Temp PSC PSC Fill
## Min. :57.00 Min. :128.6 Min. :0.00200 Min. :0.0000
## 1st Qu.:65.60 1st Qu.:138.4 1st Qu.:0.04800 1st Qu.:0.1000
## Median :68.20 Median :140.8 Median :0.07600 Median :0.1800
## Mean :68.19 Mean :141.1 Mean :0.08457 Mean :0.1954
## 3rd Qu.:70.60 3rd Qu.:143.8 3rd Qu.:0.11200 3rd Qu.:0.2600
## Max. :79.40 Max. :154.0 Max. :0.27000 Max. :0.6200
## NA's :27 NA's :26 NA's :33 NA's :23
## PSC CO2 Mnf Flow Carb Pressure1 Fill Pressure
## Min. :0.00000 Min. :-100.20 Min. :105.6 Min. :34.60
## 1st Qu.:0.02000 1st Qu.:-100.00 1st Qu.:119.0 1st Qu.:46.00
## Median :0.04000 Median : 65.20 Median :123.2 Median :46.40
## Mean :0.05641 Mean : 24.57 Mean :122.6 Mean :47.92
## 3rd Qu.:0.08000 3rd Qu.: 140.80 3rd Qu.:125.4 3rd Qu.:50.00
## Max. :0.24000 Max. : 229.40 Max. :140.2 Max. :60.40
## NA's :39 NA's :2 NA's :32 NA's :22
## Hyd Pressure1 Hyd Pressure2 Hyd Pressure3 Hyd Pressure4
## Min. :-0.80 Min. : 0.00 Min. :-1.20 Min. : 52.00
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 86.00
## Median :11.40 Median :28.60 Median :27.60 Median : 96.00
## Mean :12.44 Mean :20.96 Mean :20.46 Mean : 96.29
## 3rd Qu.:20.20 3rd Qu.:34.60 3rd Qu.:33.40 3rd Qu.:102.00
## Max. :58.00 Max. :59.40 Max. :50.00 Max. :142.00
## NA's :11 NA's :15 NA's :15 NA's :30
## Filler Level Filler Speed Temperature Usage cont
## Min. : 55.8 Min. : 998 Min. :63.60 Min. :12.08
## 1st Qu.: 98.3 1st Qu.:3888 1st Qu.:65.20 1st Qu.:18.36
## Median :118.4 Median :3982 Median :65.60 Median :21.79
## Mean :109.3 Mean :3687 Mean :65.97 Mean :20.99
## 3rd Qu.:120.0 3rd Qu.:3998 3rd Qu.:66.40 3rd Qu.:23.75
## Max. :161.2 Max. :4030 Max. :76.20 Max. :25.90
## NA's :20 NA's :57 NA's :14 NA's :5
## Carb Flow Density MFR Balling
## Min. : 26 Min. :0.240 Min. : 31.4 Min. :-0.170
## 1st Qu.:1144 1st Qu.:0.900 1st Qu.:706.3 1st Qu.: 1.496
## Median :3028 Median :0.980 Median :724.0 Median : 1.648
## Mean :2468 Mean :1.174 Mean :704.0 Mean : 2.198
## 3rd Qu.:3186 3rd Qu.:1.620 3rd Qu.:731.0 3rd Qu.: 3.292
## Max. :5104 Max. :1.920 Max. :868.6 Max. : 4.012
## NA's :2 NA's :1 NA's :212 NA's :1
## Pressure Vacuum PH Oxygen Filler Bowl Setpoint
## Min. :-6.600 Min. :7.880 Min. :0.00240 Min. : 70.0
## 1st Qu.:-5.600 1st Qu.:8.440 1st Qu.:0.02200 1st Qu.:100.0
## Median :-5.400 Median :8.540 Median :0.03340 Median :120.0
## Mean :-5.216 Mean :8.546 Mean :0.04684 Mean :109.3
## 3rd Qu.:-5.000 3rd Qu.:8.680 3rd Qu.:0.06000 3rd Qu.:120.0
## Max. :-3.600 Max. :9.360 Max. :0.40000 Max. :140.0
## NA's :4 NA's :12 NA's :2
## Pressure Setpoint Air Pressurer Alch Rel Carb Rel
## Min. :44.00 Min. :140.8 Min. :5.280 Min. :4.960
## 1st Qu.:46.00 1st Qu.:142.2 1st Qu.:6.540 1st Qu.:5.340
## Median :46.00 Median :142.6 Median :6.560 Median :5.400
## Mean :47.62 Mean :142.8 Mean :6.897 Mean :5.437
## 3rd Qu.:50.00 3rd Qu.:143.0 3rd Qu.:7.240 3rd Qu.:5.540
## Max. :52.00 Max. :148.2 Max. :8.620 Max. :6.060
## NA's :12 NA's :9 NA's :10
## Balling Lvl
## Min. :0.00
## 1st Qu.:1.38
## Median :1.48
## Mean :2.05
## 3rd Qu.:3.14
## Max. :3.66
## NA's :1
## Brand Code Carb Volume Fill Ounces PC Volume
## Length:267 Min. :5.147 Min. :23.75 Min. :0.09867
## Class :character 1st Qu.:5.287 1st Qu.:23.92 1st Qu.:0.23333
## Mode :character Median :5.340 Median :23.97 Median :0.27533
## Mean :5.369 Mean :23.97 Mean :0.27769
## 3rd Qu.:5.465 3rd Qu.:24.01 3rd Qu.:0.32200
## Max. :5.667 Max. :24.20 Max. :0.46400
## NA's :1 NA's :6 NA's :4
## Carb Pressure Carb Temp PSC PSC Fill
## Min. :60.20 Min. :130.0 Min. :0.00400 Min. :0.0200
## 1st Qu.:65.30 1st Qu.:138.4 1st Qu.:0.04450 1st Qu.:0.1000
## Median :68.00 Median :140.8 Median :0.07600 Median :0.1800
## Mean :68.25 Mean :141.2 Mean :0.08545 Mean :0.1903
## 3rd Qu.:70.60 3rd Qu.:143.8 3rd Qu.:0.11200 3rd Qu.:0.2600
## Max. :77.60 Max. :154.0 Max. :0.24600 Max. :0.6200
## NA's :1 NA's :5 NA's :3
## PSC CO2 Mnf Flow Carb Pressure1 Fill Pressure
## Min. :0.00000 Min. :-100.20 Min. :113.0 Min. :37.80
## 1st Qu.:0.02000 1st Qu.:-100.00 1st Qu.:120.2 1st Qu.:46.00
## Median :0.04000 Median : 0.20 Median :123.4 Median :47.80
## Mean :0.05107 Mean : 21.03 Mean :123.0 Mean :48.14
## 3rd Qu.:0.06000 3rd Qu.: 141.30 3rd Qu.:125.5 3rd Qu.:50.20
## Max. :0.24000 Max. : 220.40 Max. :136.0 Max. :60.20
## NA's :5 NA's :4 NA's :2
## Hyd Pressure1 Hyd Pressure2 Hyd Pressure3 Hyd Pressure4
## Min. :-50.00 Min. :-50.00 Min. :-50.00 Min. : 68.00
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 90.00
## Median : 10.40 Median : 26.80 Median : 27.70 Median : 98.00
## Mean : 12.01 Mean : 20.11 Mean : 19.61 Mean : 97.84
## 3rd Qu.: 20.40 3rd Qu.: 34.80 3rd Qu.: 33.00 3rd Qu.:104.00
## Max. : 50.00 Max. : 61.40 Max. : 49.20 Max. :140.00
## NA's :1 NA's :1 NA's :4
## Filler Level Filler Speed Temperature Usage cont
## Min. : 69.2 Min. :1006 Min. :63.80 Min. :12.90
## 1st Qu.:100.6 1st Qu.:3812 1st Qu.:65.40 1st Qu.:18.12
## Median :118.6 Median :3978 Median :65.80 Median :21.44
## Mean :110.3 Mean :3581 Mean :66.23 Mean :20.90
## 3rd Qu.:120.2 3rd Qu.:3996 3rd Qu.:66.60 3rd Qu.:23.74
## Max. :153.2 Max. :4020 Max. :75.40 Max. :24.60
## NA's :2 NA's :10 NA's :2 NA's :2
## Carb Flow Density MFR Balling
## Min. : 0 Min. :0.060 Min. : 15.6 Min. :0.902
## 1st Qu.:1083 1st Qu.:0.920 1st Qu.:707.0 1st Qu.:1.498
## Median :3038 Median :0.980 Median :724.6 Median :1.648
## Mean :2409 Mean :1.177 Mean :697.8 Mean :2.203
## 3rd Qu.:3215 3rd Qu.:1.600 3rd Qu.:731.5 3rd Qu.:3.242
## Max. :3858 Max. :1.840 Max. :784.8 Max. :3.788
## NA's :1 NA's :31 NA's :1
## Pressure Vacuum PH Oxygen Filler Bowl Setpoint
## Min. :-6.400 Mode:logical Min. :0.00240 Min. : 70.0
## 1st Qu.:-5.600 NA's:267 1st Qu.:0.01960 1st Qu.:100.0
## Median :-5.200 Median :0.03370 Median :120.0
## Mean :-5.174 Mean :0.04666 Mean :109.6
## 3rd Qu.:-4.800 3rd Qu.:0.05440 3rd Qu.:120.0
## Max. :-3.600 Max. :0.39800 Max. :130.0
## NA's :1 NA's :3 NA's :1
## Pressure Setpoint Air Pressurer Alch Rel Carb Rel
## Min. :44.00 Min. :141.2 Min. :6.400 Min. :5.18
## 1st Qu.:46.00 1st Qu.:142.2 1st Qu.:6.540 1st Qu.:5.34
## Median :46.00 Median :142.6 Median :6.580 Median :5.40
## Mean :47.73 Mean :142.8 Mean :6.907 Mean :5.44
## 3rd Qu.:50.00 3rd Qu.:142.8 3rd Qu.:7.180 3rd Qu.:5.56
## Max. :52.00 Max. :147.2 Max. :7.820 Max. :5.74
## NA's :2 NA's :1 NA's :3 NA's :2
## Balling Lvl
## Min. :0.000
## 1st Qu.:1.380
## Median :1.480
## Mean :2.051
## 3rd Qu.:3.080
## Max. :3.420
##
## Loading required package: colorspace
## Loading required package: grid
## Loading required package: data.table
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
## The following object is masked from 'package:purrr':
##
## transpose
## VIM is ready to use.
## Since version 4.0.0 the GUI is in its own package VIMGUI.
##
## Please use the package to use the new (and old) GUI.
## Suggestions and bug-reports can be submitted at: https://github.com/alexkowa/VIM/issues
##
## Attaching package: 'VIM'
## The following object is masked from 'package:datasets':
##
## sleep
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
## [1] "MFR" "Hyd Pressure2" "Carb Flow"
## [4] "Alch Rel" "Pressure Setpoint" "Hyd Pressure4"
## [7] "Filler Level" "Carb Pressure"
## NULL
Models
Linear Model
##
## Call:
## lm(formula = PH ~ ., data = training)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0343 -0.4551 0.0580 0.5136 4.3194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.237601 0.107999 -2.200 0.027935 *
## `Brand Code`B 0.427657 0.157147 2.721 0.006565 **
## `Brand Code`C -0.363806 0.156550 -2.324 0.020244 *
## `Brand Code`D 0.218253 0.114130 1.912 0.055998 .
## `Carb Volume` -0.069745 0.053135 -1.313 0.189492
## `Fill Ounces` -0.021398 0.019274 -1.110 0.267058
## `PC Volume` -0.033261 0.022633 -1.470 0.141859
## `Carb Pressure` 0.032014 0.075389 0.425 0.671141
## `Carb Temp` -0.011058 0.068520 -0.161 0.871813
## PSC -0.009890 0.019671 -0.503 0.615191
## `PSC Fill` -0.013664 0.019609 -0.697 0.486009
## `PSC CO2` -0.050539 0.019183 -2.635 0.008496 **
## `Mnf Flow` -0.490367 0.037980 -12.911 < 2e-16 ***
## `Carb Pressure1` 0.179711 0.022661 7.931 3.84e-15 ***
## `Fill Pressure` 0.023679 0.027342 0.866 0.386589
## `Hyd Pressure1` -0.009718 0.032485 -0.299 0.764861
## `Hyd Pressure2` -0.113660 0.061288 -1.855 0.063831 .
## `Hyd Pressure3` 0.330101 0.064622 5.108 3.60e-07 ***
## `Hyd Pressure4` -0.008954 0.028674 -0.312 0.754884
## `Filler Level` -0.103723 0.066403 -1.562 0.118461
## `Filler Speed` -0.003033 0.041771 -0.073 0.942134
## Temperature -0.145567 0.021840 -6.665 3.52e-11 ***
## `Usage cont` -0.118484 0.023577 -5.025 5.53e-07 ***
## `Carb Flow` 0.060476 0.027289 2.216 0.026807 *
## Density -0.326131 0.071855 -4.539 6.04e-06 ***
## MFR -0.008168 0.030348 -0.269 0.787847
## Balling -0.468198 0.164158 -2.852 0.004393 **
## `Pressure Vacuum` -0.068021 0.031350 -2.170 0.030162 *
## `Oxygen Filler` -0.061075 0.022415 -2.725 0.006499 **
## `Bowl Setpoint` 0.270312 0.067043 4.032 5.77e-05 ***
## `Pressure Setpoint` -0.104319 0.028327 -3.683 0.000238 ***
## `Air Pressurer` -0.021473 0.019730 -1.088 0.276589
## `Alch Rel` 0.201128 0.088085 2.283 0.022529 *
## `Carb Rel` 0.098093 0.042634 2.301 0.021518 *
## `Balling Lvl` 0.641993 0.159050 4.036 5.66e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7714 on 1764 degrees of freedom
## Multiple R-squared: 0.4098, Adjusted R-squared: 0.3984
## F-statistic: 36.02 on 34 and 1764 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = PH ~ ., data = training2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0295 -0.4547 0.0636 0.5205 4.3623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.272095 0.104289 -2.609 0.00916 **
## `Brand Code`B 0.405805 0.156079 2.600 0.00940 **
## `Brand Code`C -0.390068 0.155980 -2.501 0.01248 *
## `Brand Code`D 0.425019 0.077393 5.492 4.56e-08 ***
## `Carb Volume` -0.067134 0.036452 -1.842 0.06569 .
## `Fill Ounces` -0.023372 0.019213 -1.216 0.22396
## `PC Volume` -0.018939 0.022285 -0.850 0.39554
## `Carb Temp` 0.016332 0.019042 0.858 0.39117
## PSC -0.012657 0.019697 -0.643 0.52058
## `PSC Fill` -0.008865 0.019630 -0.452 0.65161
## `PSC CO2` -0.049792 0.019169 -2.597 0.00947 **
## `Mnf Flow` -0.503215 0.036851 -13.655 < 2e-16 ***
## `Carb Pressure1` 0.161144 0.021990 7.328 3.53e-13 ***
## `Fill Pressure` 0.044970 0.026002 1.729 0.08390 .
## `Hyd Pressure1` -0.037486 0.028923 -1.296 0.19512
## `Hyd Pressure3` 0.252431 0.043821 5.760 9.87e-09 ***
## `Filler Speed` 0.005471 0.024055 0.227 0.82011
## Temperature -0.143339 0.021753 -6.589 5.80e-11 ***
## `Usage cont` -0.139718 0.022970 -6.083 1.45e-09 ***
## Density -0.319312 0.068128 -4.687 2.99e-06 ***
## Balling -0.393497 0.154592 -2.545 0.01100 *
## `Pressure Vacuum` -0.045583 0.030262 -1.506 0.13218
## `Oxygen Filler` -0.060703 0.022435 -2.706 0.00688 **
## `Bowl Setpoint` 0.148406 0.027263 5.443 5.96e-08 ***
## `Pressure Setpoint` -0.109947 0.027859 -3.947 8.24e-05 ***
## `Air Pressurer` -0.015809 0.019582 -0.807 0.41957
## `Carb Rel` 0.115471 0.041723 2.768 0.00571 **
## `Balling Lvl` 0.676532 0.154086 4.391 1.20e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7744 on 1771 degrees of freedom
## Multiple R-squared: 0.4029, Adjusted R-squared: 0.3938
## F-statistic: 44.26 on 27 and 1771 DF, p-value: < 2.2e-16
MARS MODEL
## Loading required package: Formula
## Loading required package: plotmo
## Loading required package: plotrix
##
## Attaching package: 'plotrix'
## The following object is masked from 'package:psych':
##
## rescale
## Loading required package: TeachingDemos
## Multivariate Adaptive Regression Spline
##
## 1799 samples
## 25 predictor
##
## Pre-processing: centered (27), scaled (27)
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 1799, 1799, 1799, 1799, 1799, 1799, ...
## Resampling results across tuning parameters:
##
## degree nprune RMSE Rsquared MAE
## 1 2 0.8867206 0.2103277 0.6956737
## 1 3 0.8535987 0.2679548 0.6692461
## 1 4 0.8345518 0.3005094 0.6538934
## 1 5 0.8271336 0.3129571 0.6469357
## 1 6 0.8172052 0.3297803 0.6368058
## 1 7 0.8093723 0.3426488 0.6303815
## 1 8 0.8003398 0.3570452 0.6222729
## 1 9 0.7941340 0.3675287 0.6155937
## 1 10 0.7890103 0.3755941 0.6107297
## 2 2 0.8851293 0.2131670 0.6936721
## 2 3 0.8438199 0.2844008 0.6621597
## 2 4 0.8394388 0.2922949 0.6549625
## 2 5 0.8258897 0.3152327 0.6401502
## 2 6 0.8152442 0.3327858 0.6297584
## 2 7 0.8047282 0.3505888 0.6188240
## 2 8 0.7982001 0.3609115 0.6123209
## 2 9 0.7914027 0.3727390 0.6059591
## 2 10 0.7811561 0.3880791 0.5989128
## 3 2 0.8855582 0.2124518 0.6943201
## 3 3 0.8446958 0.2835561 0.6617109
## 3 4 0.8394390 0.2932133 0.6526675
## 3 5 0.8285507 0.3114026 0.6413335
## 3 6 0.8159166 0.3324821 0.6279260
## 3 7 0.8046113 0.3513653 0.6166450
## 3 8 0.7959696 0.3653488 0.6079498
## 3 9 0.7887555 0.3769430 0.6025707
## 3 10 0.7817335 0.3885946 0.5965610
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were nprune = 10 and degree = 2.
## Call: earth(x=matrix[1799,27], y=c(-1.076,-1.656...), keepxy=TRUE,
## degree=2, nprune=10)
##
## coefficients
## (Intercept) -0.8622121
## h(-0.198196-Mnf Flow) 0.9016834
## h(Bowl Setpoint- -1.27468) 0.3874775
## Brand CodeC * Brand CodeD -0.5394923
## Brand CodeC * h(1.01607-Mnf Flow) -0.2493223
## Brand CodeC * h(0.777096-Oxygen Filler) -0.3407226
## h(-0.198196-Mnf Flow) * h(Pressure Vacuum-0.384157) -0.3327478
## h(Carb Pressure1- -1.76432) * h(1.69745-Temperature) 0.1238057
## h(1.69745-Temperature) * h(Usage cont-0.278805) -0.3139828
## h(Density- -1.56239) * h(Bowl Setpoint- -1.27468) -0.1258975
##
## Selected 10 of 40 terms, and 10 of 27 predictors
## Termination condition: RSq changed by less than 0.001 at 40 terms
## Importance: `MnfFlow`, `BrandCode`C, `CarbPressure1`, Temperature, ...
## Number of terms at each degree of interaction: 1 2 7
## GCV 0.5784968 RSS 1013.704 GRSq 0.4154956 RSq 0.4300329
## k-Nearest Neighbors
##
## 1799 samples
## 32 predictor
##
## Pre-processing: centered (34), scaled (34)
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 1799, 1799, 1799, 1799, 1799, 1799, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 0.7796958 0.4113459 0.5775863
## 7 0.7638787 0.4251318 0.5712149
## 9 0.7599982 0.4268774 0.5729033
## 11 0.7558666 0.4317693 0.5719822
## 13 0.7548540 0.4325487 0.5730885
## 15 0.7564670 0.4303707 0.5763074
## 17 0.7585103 0.4274200 0.5787186
## 19 0.7615933 0.4227111 0.5825155
## 21 0.7637627 0.4194944 0.5851491
## 23 0.7672652 0.4142545 0.5886519
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 13.
Bagged Tree Model
bagControl = bagControl(fit = ctreeBag$fit, predict = ctreeBag$pred, aggregate = ctreeBag$aggregate)
bag_model <- train(PH ~.,
data = training2, method="bag", bagControl = bagControl,
center = TRUE,
scale = TRUE,
trControl = trainControl("cv", number = 5),
tuneLength = 25,na.action = na.exclude)
## Warning: executing %dopar% sequentially: no parallel backend registered
## Bagged Model
##
## 1799 samples
## 25 predictor
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 1438, 1439, 1440, 1439, 1440
## Resampling results:
##
## RMSE Rsquared MAE
## 0.7086387 0.4946152 0.5321531
##
## Tuning parameter 'vars' was held constant at a value of 27
## Stochastic Gradient Boosting
##
## 1799 samples
## 32 predictor
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 1439, 1439, 1439, 1439, 1440
## Resampling results across tuning parameters:
##
## interaction.depth n.trees RMSE Rsquared MAE
## 1 50 0.7940863 0.3787615 0.6260608
## 1 100 0.7720805 0.4057486 0.6077037
## 1 150 0.7660696 0.4121970 0.6007820
## 1 200 0.7602647 0.4186988 0.5951930
## 1 250 0.7584085 0.4205916 0.5898030
## 1 300 0.7581452 0.4204349 0.5871813
## 1 350 0.7570953 0.4225198 0.5859415
## 1 400 0.7572114 0.4221444 0.5848688
## 1 450 0.7566378 0.4232554 0.5834376
## 1 500 0.7572621 0.4224765 0.5828689
## 1 550 0.7570439 0.4227299 0.5819140
## 1 600 0.7573377 0.4226626 0.5802516
## 1 650 0.7585327 0.4212666 0.5817796
## 1 700 0.7599010 0.4191890 0.5820626
## 1 750 0.7609231 0.4183584 0.5821235
## 1 800 0.7608742 0.4181826 0.5820341
## 1 850 0.7594892 0.4205481 0.5798933
## 1 900 0.7583318 0.4220531 0.5788770
## 1 950 0.7572313 0.4237875 0.5778177
## 1 1000 0.7584548 0.4226046 0.5789300
## 2 50 0.7543461 0.4392164 0.5927526
## 2 100 0.7312263 0.4656035 0.5701011
## 2 150 0.7182285 0.4817352 0.5545016
## 2 200 0.7147113 0.4858075 0.5487336
## 2 250 0.7091846 0.4932229 0.5434720
## 2 300 0.7047382 0.4989244 0.5389097
## 2 350 0.7023769 0.5022009 0.5362863
## 2 400 0.7003822 0.5050079 0.5333657
## 2 450 0.7005464 0.5050875 0.5318027
## 2 500 0.6987470 0.5078935 0.5287474
## 2 550 0.6959495 0.5115275 0.5267737
## 2 600 0.6942135 0.5140517 0.5255325
## 2 650 0.6941006 0.5145047 0.5253628
## 2 700 0.6930339 0.5161452 0.5235945
## 2 750 0.6922977 0.5172888 0.5231254
## 2 800 0.6908824 0.5194937 0.5220396
## 2 850 0.6891750 0.5223704 0.5204442
## 2 900 0.6884157 0.5233400 0.5209834
## 2 950 0.6886573 0.5231230 0.5207102
## 2 1000 0.6886393 0.5234906 0.5209244
## 3 50 0.7313184 0.4700763 0.5705301
## 3 100 0.7099989 0.4946587 0.5461014
## 3 150 0.6966210 0.5122258 0.5333464
## 3 200 0.6895426 0.5215631 0.5241350
## 3 250 0.6868288 0.5246612 0.5218729
## 3 300 0.6846589 0.5271672 0.5194405
## 3 350 0.6821946 0.5305856 0.5164303
## 3 400 0.6810621 0.5321973 0.5165130
## 3 450 0.6789900 0.5350761 0.5143812
## 3 500 0.6760289 0.5395233 0.5117238
## 3 550 0.6744592 0.5417717 0.5105462
## 3 600 0.6745687 0.5419550 0.5103554
## 3 650 0.6739971 0.5428248 0.5099355
## 3 700 0.6729303 0.5444093 0.5094492
## 3 750 0.6739381 0.5433350 0.5086350
## 3 800 0.6723289 0.5456597 0.5075732
## 3 850 0.6712374 0.5471451 0.5076663
## 3 900 0.6712472 0.5471347 0.5077598
## 3 950 0.6715778 0.5470054 0.5073943
## 3 1000 0.6716372 0.5472059 0.5070168
## 4 50 0.7141166 0.4949271 0.5529603
## 4 100 0.6927026 0.5180203 0.5281567
## 4 150 0.6796930 0.5345758 0.5146507
## 4 200 0.6765017 0.5383714 0.5098753
## 4 250 0.6730834 0.5428341 0.5071816
## 4 300 0.6697691 0.5472589 0.5034884
## 4 350 0.6670108 0.5511408 0.5032579
## 4 400 0.6652760 0.5534874 0.5005529
## 4 450 0.6656821 0.5531452 0.5014939
## 4 500 0.6625912 0.5572215 0.4994350
## 4 550 0.6603068 0.5603321 0.4975673
## 4 600 0.6584303 0.5628414 0.4960590
## 4 650 0.6585247 0.5628986 0.4951755
## 4 700 0.6564802 0.5660572 0.4936407
## 4 750 0.6552852 0.5676480 0.4928799
## 4 800 0.6569507 0.5659021 0.4936989
## 4 850 0.6583828 0.5641469 0.4945265
## 4 900 0.6574247 0.5655463 0.4938535
## 4 950 0.6558364 0.5675192 0.4920329
## 4 1000 0.6564775 0.5668623 0.4927122
## 5 50 0.7010221 0.5134188 0.5419663
## 5 100 0.6790268 0.5373907 0.5187372
## 5 150 0.6728390 0.5437853 0.5093348
## 5 200 0.6687110 0.5490123 0.5038351
## 5 250 0.6669736 0.5511066 0.5010368
## 5 300 0.6645482 0.5542419 0.4996366
## 5 350 0.6608626 0.5596343 0.4958318
## 5 400 0.6581104 0.5633457 0.4941709
## 5 450 0.6555269 0.5667904 0.4917419
## 5 500 0.6556817 0.5669356 0.4901420
## 5 550 0.6568323 0.5654803 0.4902174
## 5 600 0.6567724 0.5658471 0.4891532
## 5 650 0.6559812 0.5670393 0.4883037
## 5 700 0.6550979 0.5681990 0.4872684
## 5 750 0.6535647 0.5702699 0.4856225
## 5 800 0.6531674 0.5709217 0.4857768
## 5 850 0.6532799 0.5710125 0.4850259
## 5 900 0.6530060 0.5714792 0.4849249
## 5 950 0.6517552 0.5731882 0.4841762
## 5 1000 0.6520510 0.5728490 0.4843703
## 6 50 0.6895890 0.5285651 0.5321773
## 6 100 0.6696637 0.5501136 0.5129311
## 6 150 0.6569762 0.5648298 0.5037176
## 6 200 0.6562873 0.5655389 0.5018840
## 6 250 0.6516742 0.5713194 0.4959884
## 6 300 0.6488227 0.5748592 0.4924938
## 6 350 0.6473555 0.5771933 0.4910339
## 6 400 0.6462240 0.5785872 0.4881264
## 6 450 0.6457796 0.5793279 0.4869715
## 6 500 0.6451688 0.5802738 0.4853244
## 6 550 0.6444413 0.5815288 0.4842715
## 6 600 0.6431196 0.5833371 0.4832606
## 6 650 0.6422719 0.5845874 0.4819869
## 6 700 0.6428311 0.5839605 0.4823249
## 6 750 0.6432157 0.5834869 0.4823709
## 6 800 0.6428258 0.5840653 0.4826485
## 6 850 0.6432975 0.5835653 0.4825560
## 6 900 0.6437221 0.5832501 0.4825209
## 6 950 0.6434525 0.5835831 0.4814506
## 6 1000 0.6437303 0.5833468 0.4819381
## 7 50 0.6815269 0.5383920 0.5261737
## 7 100 0.6611320 0.5600783 0.5030333
## 7 150 0.6539961 0.5684006 0.4949588
## 7 200 0.6478703 0.5764237 0.4902473
## 7 250 0.6472636 0.5774415 0.4882592
## 7 300 0.6445572 0.5808674 0.4853508
## 7 350 0.6433541 0.5822724 0.4845676
## 7 400 0.6438594 0.5819613 0.4841994
## 7 450 0.6438007 0.5824004 0.4846855
## 7 500 0.6427438 0.5838912 0.4840385
## 7 550 0.6422597 0.5846059 0.4831335
## 7 600 0.6433281 0.5835040 0.4839372
## 7 650 0.6439623 0.5829136 0.4847471
## 7 700 0.6434631 0.5835791 0.4836487
## 7 750 0.6424360 0.5850511 0.4826555
## 7 800 0.6416839 0.5860486 0.4823239
## 7 850 0.6414045 0.5864734 0.4816313
## 7 900 0.6411403 0.5867191 0.4814372
## 7 950 0.6413640 0.5864777 0.4812171
## 7 1000 0.6414010 0.5864486 0.4816128
## 8 50 0.6743416 0.5484627 0.5165858
## 8 100 0.6509402 0.5743293 0.4945704
## 8 150 0.6407950 0.5858746 0.4832314
## 8 200 0.6360365 0.5915323 0.4781552
## 8 250 0.6330010 0.5954976 0.4743225
## 8 300 0.6307508 0.5982106 0.4705235
## 8 350 0.6299098 0.5993228 0.4710306
## 8 400 0.6288441 0.6009037 0.4693306
## 8 450 0.6293942 0.6001548 0.4695619
## 8 500 0.6293250 0.6004752 0.4701527
## 8 550 0.6294555 0.6006597 0.4700233
## 8 600 0.6299880 0.6001407 0.4703696
## 8 650 0.6297955 0.6005190 0.4702786
## 8 700 0.6304551 0.5998544 0.4708125
## 8 750 0.6302398 0.6001394 0.4701407
## 8 800 0.6303476 0.6001049 0.4698411
## 8 850 0.6304263 0.6000109 0.4699052
## 8 900 0.6301426 0.6004596 0.4699979
## 8 950 0.6301886 0.6004498 0.4697398
## 8 1000 0.6304620 0.6000715 0.4698815
## 9 50 0.6653988 0.5598878 0.5081606
## 9 100 0.6498457 0.5750205 0.4927864
## 9 150 0.6448816 0.5805781 0.4855998
## 9 200 0.6416281 0.5842109 0.4833593
## 9 250 0.6387874 0.5879300 0.4813083
## 9 300 0.6373810 0.5897138 0.4797866
## 9 350 0.6349952 0.5927529 0.4777069
## 9 400 0.6331475 0.5950578 0.4759286
## 9 450 0.6322341 0.5962575 0.4752481
## 9 500 0.6316605 0.5970087 0.4746489
## 9 550 0.6315416 0.5973033 0.4737805
## 9 600 0.6318515 0.5970063 0.4741805
## 9 650 0.6315515 0.5974930 0.4742558
## 9 700 0.6315447 0.5975161 0.4744583
## 9 750 0.6315529 0.5974907 0.4737504
## 9 800 0.6315327 0.5976058 0.4737999
## 9 850 0.6306112 0.5987382 0.4727761
## 9 900 0.6308837 0.5984757 0.4730289
## 9 950 0.6309178 0.5984796 0.4727729
## 9 1000 0.6308070 0.5986314 0.4725955
## 10 50 0.6672997 0.5557771 0.5100692
## 10 100 0.6551864 0.5673443 0.4949370
## 10 150 0.6486640 0.5754789 0.4883327
## 10 200 0.6440296 0.5815974 0.4844364
## 10 250 0.6419835 0.5842401 0.4830858
## 10 300 0.6395900 0.5873723 0.4797501
## 10 350 0.6380403 0.5894740 0.4782991
## 10 400 0.6366961 0.5912211 0.4773800
## 10 450 0.6366195 0.5915257 0.4766102
## 10 500 0.6352940 0.5931987 0.4748476
## 10 550 0.6362610 0.5921119 0.4752443
## 10 600 0.6365020 0.5919732 0.4757126
## 10 650 0.6370440 0.5912671 0.4762290
## 10 700 0.6372324 0.5911979 0.4762483
## 10 750 0.6367087 0.5918044 0.4758029
## 10 800 0.6365018 0.5921429 0.4754619
## 10 850 0.6364130 0.5922833 0.4753258
## 10 900 0.6364991 0.5921621 0.4751995
## 10 950 0.6363430 0.5923875 0.4747992
## 10 1000 0.6361904 0.5925847 0.4745889
## 11 50 0.6637283 0.5589972 0.5035067
## 11 100 0.6479751 0.5763744 0.4885245
## 11 150 0.6376352 0.5893908 0.4790005
## 11 200 0.6373872 0.5898933 0.4795619
## 11 250 0.6336705 0.5946110 0.4771309
## 11 300 0.6336061 0.5948792 0.4765587
## 11 350 0.6319020 0.5970334 0.4739264
## 11 400 0.6309953 0.5982399 0.4724554
## 11 450 0.6313442 0.5979111 0.4727891
## 11 500 0.6316577 0.5975869 0.4727295
## 11 550 0.6317882 0.5974477 0.4726572
## 11 600 0.6312834 0.5981240 0.4722306
## 11 650 0.6310130 0.5984997 0.4718476
## 11 700 0.6304559 0.5991858 0.4715117
## 11 750 0.6305062 0.5991486 0.4712975
## 11 800 0.6305647 0.5991290 0.4711583
## 11 850 0.6304182 0.5993320 0.4712171
## 11 900 0.6300503 0.5997640 0.4708547
## 11 950 0.6301977 0.5995741 0.4709217
## 11 1000 0.6302487 0.5995286 0.4710061
## 12 50 0.6625446 0.5618382 0.4992600
## 12 100 0.6452991 0.5806773 0.4842649
## 12 150 0.6397389 0.5871918 0.4785054
## 12 200 0.6398262 0.5868024 0.4777340
## 12 250 0.6383316 0.5885135 0.4755537
## 12 300 0.6360392 0.5915203 0.4740776
## 12 350 0.6342028 0.5938924 0.4721237
## 12 400 0.6334482 0.5948655 0.4706047
## 12 450 0.6337871 0.5944656 0.4708441
## 12 500 0.6335257 0.5948480 0.4707309
## 12 550 0.6327589 0.5957665 0.4700928
## 12 600 0.6323707 0.5963298 0.4698991
## 12 650 0.6319040 0.5969609 0.4693943
## 12 700 0.6320646 0.5967830 0.4694157
## 12 750 0.6321819 0.5966641 0.4693776
## 12 800 0.6323883 0.5964109 0.4694895
## 12 850 0.6322276 0.5966445 0.4691631
## 12 900 0.6320067 0.5969190 0.4690007
## 12 950 0.6319915 0.5969546 0.4688224
## 12 1000 0.6320999 0.5968290 0.4688976
## 13 50 0.6530382 0.5731567 0.4963740
## 13 100 0.6409785 0.5858182 0.4844564
## 13 150 0.6386480 0.5881768 0.4810303
## 13 200 0.6350965 0.5925414 0.4752586
## 13 250 0.6340065 0.5941265 0.4731515
## 13 300 0.6338134 0.5942956 0.4717774
## 13 350 0.6334163 0.5949196 0.4713827
## 13 400 0.6318425 0.5969414 0.4707126
## 13 450 0.6316813 0.5972654 0.4700285
## 13 500 0.6317466 0.5972522 0.4696822
## 13 550 0.6315873 0.5975527 0.4696678
## 13 600 0.6312374 0.5980113 0.4693953
## 13 650 0.6311433 0.5981722 0.4694274
## 13 700 0.6308254 0.5985794 0.4692756
## 13 750 0.6307363 0.5987135 0.4689086
## 13 800 0.6305085 0.5989794 0.4688086
## 13 850 0.6305870 0.5989130 0.4687831
## 13 900 0.6307837 0.5986777 0.4689020
## 13 950 0.6307011 0.5987920 0.4688024
## 13 1000 0.6307473 0.5987411 0.4687918
## 14 50 0.6421990 0.5893094 0.4881080
## 14 100 0.6359272 0.5926956 0.4793784
## 14 150 0.6318247 0.5973020 0.4730428
## 14 200 0.6285945 0.6014474 0.4714381
## 14 250 0.6279109 0.6024122 0.4704781
## 14 300 0.6262379 0.6047361 0.4683470
## 14 350 0.6258434 0.6053589 0.4675324
## 14 400 0.6257910 0.6054434 0.4670927
## 14 450 0.6251759 0.6063712 0.4667500
## 14 500 0.6259411 0.6054707 0.4673077
## 14 550 0.6259643 0.6054375 0.4671738
## 14 600 0.6258854 0.6055921 0.4669004
## 14 650 0.6259469 0.6055170 0.4669554
## 14 700 0.6257160 0.6057517 0.4666790
## 14 750 0.6257700 0.6056971 0.4667734
## 14 800 0.6257784 0.6056630 0.4666410
## 14 850 0.6258974 0.6055291 0.4667359
## 14 900 0.6258441 0.6055947 0.4665939
## 14 950 0.6258078 0.6056613 0.4665970
## 14 1000 0.6258534 0.6056090 0.4666476
## 15 50 0.6466405 0.5816747 0.4869157
## 15 100 0.6367262 0.5909857 0.4768146
## 15 150 0.6362986 0.5910301 0.4756273
## 15 200 0.6357242 0.5919382 0.4750880
## 15 250 0.6324705 0.5962026 0.4724182
## 15 300 0.6314381 0.5975711 0.4722730
## 15 350 0.6317789 0.5972661 0.4716165
## 15 400 0.6324475 0.5964755 0.4720450
## 15 450 0.6318574 0.5971986 0.4718860
## 15 500 0.6310692 0.5981880 0.4711371
## 15 550 0.6310996 0.5981156 0.4709605
## 15 600 0.6309213 0.5983717 0.4708285
## 15 650 0.6307618 0.5985618 0.4704831
## 15 700 0.6309515 0.5983738 0.4706997
## 15 750 0.6311117 0.5981821 0.4707738
## 15 800 0.6311141 0.5981673 0.4707193
## 15 850 0.6310015 0.5983138 0.4706583
## 15 900 0.6308958 0.5984466 0.4705340
## 15 950 0.6309464 0.5983801 0.4706423
## 15 1000 0.6309470 0.5983784 0.4706059
## 16 50 0.6402362 0.5890132 0.4856879
## 16 100 0.6296643 0.5996753 0.4721629
## 16 150 0.6283752 0.6013113 0.4691445
## 16 200 0.6254496 0.6047207 0.4650736
## 16 250 0.6239750 0.6065685 0.4629154
## 16 300 0.6229071 0.6078516 0.4623176
## 16 350 0.6233526 0.6072790 0.4629715
## 16 400 0.6233746 0.6073441 0.4631065
## 16 450 0.6234806 0.6072637 0.4631642
## 16 500 0.6230952 0.6077560 0.4628111
## 16 550 0.6232005 0.6076396 0.4626725
## 16 600 0.6230882 0.6077728 0.4628150
## 16 650 0.6229312 0.6079555 0.4626951
## 16 700 0.6228709 0.6080382 0.4625919
## 16 750 0.6226541 0.6082974 0.4624600
## 16 800 0.6226195 0.6083465 0.4624315
## 16 850 0.6226036 0.6083756 0.4624305
## 16 900 0.6225660 0.6084264 0.4624038
## 16 950 0.6225470 0.6084549 0.4623686
## 16 1000 0.6225389 0.6084674 0.4623128
## 17 50 0.6469284 0.5798829 0.4862986
## 17 100 0.6349791 0.5930488 0.4716407
## 17 150 0.6310431 0.5982012 0.4681924
## 17 200 0.6295102 0.6002640 0.4662501
## 17 250 0.6275048 0.6029223 0.4649923
## 17 300 0.6264223 0.6042264 0.4637038
## 17 350 0.6252147 0.6058527 0.4628510
## 17 400 0.6252036 0.6058965 0.4628203
## 17 450 0.6245456 0.6067106 0.4625688
## 17 500 0.6243666 0.6070255 0.4627357
## 17 550 0.6244720 0.6069403 0.4630668
## 17 600 0.6243175 0.6071152 0.4627768
## 17 650 0.6242850 0.6071573 0.4626343
## 17 700 0.6240605 0.6074572 0.4625840
## 17 750 0.6242068 0.6072705 0.4627207
## 17 800 0.6242653 0.6072105 0.4627459
## 17 850 0.6243746 0.6070808 0.4627857
## 17 900 0.6244208 0.6070221 0.4628419
## 17 950 0.6244354 0.6070057 0.4628886
## 17 1000 0.6244106 0.6070381 0.4628644
## 18 50 0.6366267 0.5934726 0.4785109
## 18 100 0.6350115 0.5927431 0.4740650
## 18 150 0.6322341 0.5963393 0.4700647
## 18 200 0.6306243 0.5982898 0.4689072
## 18 250 0.6282367 0.6013506 0.4670827
## 18 300 0.6273296 0.6026236 0.4653708
## 18 350 0.6266833 0.6035677 0.4644081
## 18 400 0.6269613 0.6032739 0.4645472
## 18 450 0.6267337 0.6036412 0.4646247
## 18 500 0.6267944 0.6035880 0.4645911
## 18 550 0.6265530 0.6039303 0.4643194
## 18 600 0.6265369 0.6039732 0.4642760
## 18 650 0.6266737 0.6038050 0.4643596
## 18 700 0.6267421 0.6037114 0.4644504
## 18 750 0.6268859 0.6035381 0.4645438
## 18 800 0.6269422 0.6034752 0.4645951
## 18 850 0.6269653 0.6034534 0.4645964
## 18 900 0.6269635 0.6034595 0.4646075
## 18 950 0.6270498 0.6033551 0.4646890
## 18 1000 0.6270279 0.6033853 0.4646610
## 19 50 0.6396755 0.5897888 0.4777288
## 19 100 0.6265556 0.6034992 0.4672689
## 19 150 0.6233796 0.6073727 0.4650991
## 19 200 0.6221115 0.6090879 0.4639147
## 19 250 0.6205974 0.6111624 0.4628601
## 19 300 0.6200860 0.6118701 0.4622773
## 19 350 0.6193292 0.6128851 0.4613329
## 19 400 0.6192485 0.6130575 0.4612620
## 19 450 0.6194734 0.6128540 0.4615660
## 19 500 0.6194843 0.6129459 0.4612967
## 19 550 0.6196862 0.6126982 0.4613966
## 19 600 0.6198480 0.6125282 0.4615396
## 19 650 0.6198731 0.6124974 0.4615150
## 19 700 0.6198856 0.6125051 0.4615290
## 19 750 0.6198531 0.6125451 0.4615292
## 19 800 0.6198709 0.6125224 0.4615619
## 19 850 0.6198434 0.6125592 0.4614739
## 19 900 0.6198192 0.6125906 0.4614694
## 19 950 0.6198064 0.6126082 0.4614517
## 19 1000 0.6198008 0.6126169 0.4614450
## 20 50 0.6421045 0.5855041 0.4801030
## 20 100 0.6310841 0.5979748 0.4718464
## 20 150 0.6262671 0.6040727 0.4668554
## 20 200 0.6267935 0.6035826 0.4673259
## 20 250 0.6256186 0.6051039 0.4658910
## 20 300 0.6259498 0.6047808 0.4659066
## 20 350 0.6260223 0.6047259 0.4658776
## 20 400 0.6264284 0.6041898 0.4659544
## 20 450 0.6267353 0.6038262 0.4662161
## 20 500 0.6264306 0.6042401 0.4660094
## 20 550 0.6262564 0.6044344 0.4659115
## 20 600 0.6262895 0.6044109 0.4658100
## 20 650 0.6262391 0.6044686 0.4657837
## 20 700 0.6261326 0.6046089 0.4656617
## 20 750 0.6262464 0.6044618 0.4656365
## 20 800 0.6262901 0.6044153 0.4656768
## 20 850 0.6262710 0.6044414 0.4656432
## 20 900 0.6263389 0.6043599 0.4656819
## 20 950 0.6263213 0.6043854 0.4656660
## 20 1000 0.6263141 0.6043901 0.4656787
##
## Tuning parameter 'shrinkage' was held constant at a value of 0.1
##
## Tuning parameter 'n.minobsinnode' was held constant at a value of 10
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were n.trees = 400,
## interaction.depth = 19, shrinkage = 0.1 and n.minobsinnode = 10.
XGBoost
## Loading required package: xgboost
##
## Attaching package: 'xgboost'
## The following object is masked from 'package:dplyr':
##
## slice
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following object is masked from 'package:tidyr':
##
## expand
#converting datasets to matrices
#options(na.action="na.pass")
training2 <- training %>% drop_na(`Brand Code`)
testing2 <- testing %>% drop_na(`Brand Code`)
trainingmx<-model.matrix(~.+0,data=training2[,names(training2) != c("PH")])
testingmx<-model.matrix(~.+0,data=testing2[,names(testing2) != c("PH")])
trainingdmx <- xgb.DMatrix(data = trainingmx, label=training2$PH)
testingdmx <- xgb.DMatrix(data = testingmx, label=testing2$PH)
#default parameters
params <- list(booster = "gbtree", objective = "reg:linear", eta=0.3, gamma=0, max_depth=6, min_child_weight=1, subsample=1, colsample_bytree=1)
#determine the best nround parameter (It controls the maximum number of iterations. For classification, it is similar to the number of trees to grow.)
xgbcv <- xgb.cv( params = params, data = trainingdmx, nrounds = 300, nfold = 5, showsd = T, stratified = T, print_every_n = 10, early_stop_rounds = 20, maximize = F) # best at 250 iterations
## [1] train-rmse:0.896485+0.006482 test-rmse:0.930151+0.027302
## [11] train-rmse:0.349706+0.009548 test-rmse:0.637472+0.014686
## [21] train-rmse:0.253027+0.004508 test-rmse:0.625070+0.015954
## [31] train-rmse:0.197612+0.006486 test-rmse:0.620623+0.016156
## [41] train-rmse:0.153593+0.005757 test-rmse:0.617564+0.016294
## [51] train-rmse:0.120084+0.006681 test-rmse:0.616261+0.015943
## [61] train-rmse:0.091894+0.007157 test-rmse:0.617017+0.016446
## [71] train-rmse:0.072353+0.005130 test-rmse:0.617381+0.015654
## [81] train-rmse:0.058606+0.004288 test-rmse:0.617013+0.015432
## [91] train-rmse:0.046055+0.002894 test-rmse:0.616321+0.015542
## [101] train-rmse:0.035947+0.001527 test-rmse:0.615905+0.015464
## [111] train-rmse:0.029164+0.001274 test-rmse:0.615785+0.015742
## [121] train-rmse:0.022503+0.001495 test-rmse:0.615409+0.015547
## [131] train-rmse:0.017554+0.001053 test-rmse:0.615358+0.015531
## [141] train-rmse:0.014320+0.000815 test-rmse:0.615388+0.015463
## [151] train-rmse:0.011277+0.000747 test-rmse:0.615319+0.015471
## [161] train-rmse:0.008850+0.000520 test-rmse:0.615250+0.015461
## [171] train-rmse:0.007066+0.000459 test-rmse:0.615271+0.015374
## [181] train-rmse:0.005718+0.000473 test-rmse:0.615196+0.015426
## [191] train-rmse:0.004715+0.000462 test-rmse:0.615200+0.015514
## [201] train-rmse:0.003673+0.000207 test-rmse:0.615199+0.015490
## [211] train-rmse:0.002972+0.000125 test-rmse:0.615184+0.015468
## [221] train-rmse:0.002379+0.000110 test-rmse:0.615184+0.015468
## [231] train-rmse:0.001813+0.000079 test-rmse:0.615196+0.015475
## [241] train-rmse:0.001438+0.000070 test-rmse:0.615197+0.015473
## [251] train-rmse:0.001160+0.000073 test-rmse:0.615184+0.015472
## [261] train-rmse:0.001031+0.000039 test-rmse:0.615188+0.015471
## [271] train-rmse:0.001030+0.000040 test-rmse:0.615188+0.015470
## [281] train-rmse:0.001030+0.000040 test-rmse:0.615188+0.015470
## [291] train-rmse:0.001030+0.000040 test-rmse:0.615188+0.015470
## [300] train-rmse:0.001030+0.000040 test-rmse:0.615188+0.015470
## [1] val-rmse:0.941474 train-rmse:0.899213
## [11] val-rmse:0.588037 train-rmse:0.364146
## [21] val-rmse:0.587210 train-rmse:0.292580
## [31] val-rmse:0.580854 train-rmse:0.231058
## [41] val-rmse:0.580917 train-rmse:0.182077
## [51] val-rmse:0.580488 train-rmse:0.147914
## [61] val-rmse:0.581018 train-rmse:0.120995
## [71] val-rmse:0.579306 train-rmse:0.096742
## [81] val-rmse:0.580135 train-rmse:0.080349
## [91] val-rmse:0.579160 train-rmse:0.067235
## [101] val-rmse:0.578952 train-rmse:0.054838
## [111] val-rmse:0.578901 train-rmse:0.047380
## [121] val-rmse:0.579167 train-rmse:0.036021
## [131] val-rmse:0.579238 train-rmse:0.028585
## [141] val-rmse:0.579130 train-rmse:0.024385
## [151] val-rmse:0.579190 train-rmse:0.019188
## [161] val-rmse:0.579097 train-rmse:0.014893
## [171] val-rmse:0.579186 train-rmse:0.012804
## [181] val-rmse:0.579150 train-rmse:0.010257
## [191] val-rmse:0.579253 train-rmse:0.008373
## [201] val-rmse:0.579283 train-rmse:0.006820
## [211] val-rmse:0.579345 train-rmse:0.005846
## [221] val-rmse:0.579340 train-rmse:0.004861
## [231] val-rmse:0.579335 train-rmse:0.003992
## [241] val-rmse:0.579335 train-rmse:0.003237
## [251] val-rmse:0.579337 train-rmse:0.002618
## [260] val-rmse:0.579312 train-rmse:0.002276
