SuperML R package is designed to unify the model training process in R like Python. Generally, it’s seen that people spend lot of time in searching for packages, figuring out the syntax for training machine learning models in R. This behaviour is highly apparent in users who frequently switch between R and Python. This package provides a python´s scikit-learn interface (fit, predict) to train models faster.
In addition to building machine learning models, there are handy functionalities to do feature engineering
This ambitious package is my ongoing effort to help the r-community build ML models easily and faster in R.
You can install latest cran version using (recommended):
You can install the developmemt version directly from github using:
For machine learning, superml is based on the existing R packages. Hence, while installing the package, we don’t install all the dependencies. However, while training any model, superml will automatically install the package if its not found. Still, if you want to install all dependencies at once, you can simply do:
This package uses existing r-packages to build machine learning model. In this tutorial, we’ll use data.table R package to do all tasks related to data manipulation.
We’ll quickly prepare the data set to be ready to served for model training.
load("../data/reg_train.rda")
# if the above doesn't work, you can try: load("reg_train.rda")
library(data.table)
library(caret)
#> Loading required package: lattice
#> Loading required package: ggplot2
library(superml)
library(Metrics)
#>
#> Attaching package: 'Metrics'
#> The following objects are masked from 'package:caret':
#>
#> precision, recall
head(reg_train)
#> Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour
#> 1: 1 60 RL 65 8450 Pave <NA> Reg Lvl
#> 2: 2 20 RL 80 9600 Pave <NA> Reg Lvl
#> 3: 3 60 RL 68 11250 Pave <NA> IR1 Lvl
#> 4: 4 70 RL 60 9550 Pave <NA> IR1 Lvl
#> 5: 5 60 RL 84 14260 Pave <NA> IR1 Lvl
#> 6: 6 50 RL 85 14115 Pave <NA> IR1 Lvl
#> Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType
#> 1: AllPub Inside Gtl CollgCr Norm Norm 1Fam
#> 2: AllPub FR2 Gtl Veenker Feedr Norm 1Fam
#> 3: AllPub Inside Gtl CollgCr Norm Norm 1Fam
#> 4: AllPub Corner Gtl Crawfor Norm Norm 1Fam
#> 5: AllPub FR2 Gtl NoRidge Norm Norm 1Fam
#> 6: AllPub Inside Gtl Mitchel Norm Norm 1Fam
#> HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd RoofStyle RoofMatl
#> 1: 2Story 7 5 2003 2003 Gable CompShg
#> 2: 1Story 6 8 1976 1976 Gable CompShg
#> 3: 2Story 7 5 2001 2002 Gable CompShg
#> 4: 2Story 7 5 1915 1970 Gable CompShg
#> 5: 2Story 8 5 2000 2000 Gable CompShg
#> 6: 1.5Fin 5 5 1993 1995 Gable CompShg
#> Exterior1st Exterior2nd MasVnrType MasVnrArea ExterQual ExterCond Foundation
#> 1: VinylSd VinylSd BrkFace 196 Gd TA PConc
#> 2: MetalSd MetalSd None 0 TA TA CBlock
#> 3: VinylSd VinylSd BrkFace 162 Gd TA PConc
#> 4: Wd Sdng Wd Shng None 0 TA TA BrkTil
#> 5: VinylSd VinylSd BrkFace 350 Gd TA PConc
#> 6: VinylSd VinylSd None 0 TA TA Wood
#> BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2
#> 1: Gd TA No GLQ 706 Unf
#> 2: Gd TA Gd ALQ 978 Unf
#> 3: Gd TA Mn GLQ 486 Unf
#> 4: TA Gd No ALQ 216 Unf
#> 5: Gd TA Av GLQ 655 Unf
#> 6: Gd TA No GLQ 732 Unf
#> BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir Electrical
#> 1: 0 150 856 GasA Ex Y SBrkr
#> 2: 0 284 1262 GasA Ex Y SBrkr
#> 3: 0 434 920 GasA Ex Y SBrkr
#> 4: 0 540 756 GasA Gd Y SBrkr
#> 5: 0 490 1145 GasA Ex Y SBrkr
#> 6: 0 64 796 GasA Ex Y SBrkr
#> 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath
#> 1: 856 854 0 1710 1 0 2
#> 2: 1262 0 0 1262 0 1 2
#> 3: 920 866 0 1786 1 0 2
#> 4: 961 756 0 1717 1 0 1
#> 5: 1145 1053 0 2198 1 0 2
#> 6: 796 566 0 1362 1 0 1
#> HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Functional
#> 1: 1 3 1 Gd 8 Typ
#> 2: 0 3 1 TA 6 Typ
#> 3: 1 3 1 Gd 6 Typ
#> 4: 0 3 1 Gd 7 Typ
#> 5: 1 4 1 Gd 9 Typ
#> 6: 1 1 1 TA 5 Typ
#> Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish GarageCars
#> 1: 0 <NA> Attchd 2003 RFn 2
#> 2: 1 TA Attchd 1976 RFn 2
#> 3: 1 TA Attchd 2001 RFn 2
#> 4: 1 Gd Detchd 1998 Unf 3
#> 5: 1 TA Attchd 2000 RFn 3
#> 6: 0 <NA> Attchd 1993 Unf 2
#> GarageArea GarageQual GarageCond PavedDrive WoodDeckSF OpenPorchSF
#> 1: 548 TA TA Y 0 61
#> 2: 460 TA TA Y 298 0
#> 3: 608 TA TA Y 0 42
#> 4: 642 TA TA Y 0 35
#> 5: 836 TA TA Y 192 84
#> 6: 480 TA TA Y 40 30
#> EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC Fence MiscFeature
#> 1: 0 0 0 0 <NA> <NA> <NA>
#> 2: 0 0 0 0 <NA> <NA> <NA>
#> 3: 0 0 0 0 <NA> <NA> <NA>
#> 4: 272 0 0 0 <NA> <NA> <NA>
#> 5: 0 0 0 0 <NA> <NA> <NA>
#> 6: 0 320 0 0 <NA> MnPrv Shed
#> MiscVal MoSold YrSold SaleType SaleCondition SalePrice
#> 1: 0 2 2008 WD Normal 208500
#> 2: 0 5 2007 WD Normal 181500
#> 3: 0 9 2008 WD Normal 223500
#> 4: 0 2 2006 WD Abnorml 140000
#> 5: 0 12 2008 WD Normal 250000
#> 6: 700 10 2009 WD Normal 143000
split <- createDataPartition(y = reg_train$SalePrice, p = 0.7)
xtrain <- reg_train[split$Resample1]
xtest <- reg_train[!split$Resample1]# remove features with 90% or more missing values
# we will also remove the Id column because it doesn't contain
# any useful information
na_cols <- colSums(is.na(xtrain)) / nrow(xtrain)
na_cols <- names(na_cols[which(na_cols > 0.9)])
xtrain[, c(na_cols, "Id") := NULL]
xtest[, c(na_cols, "Id") := NULL]
# encode categorical variables
cat_cols <- names(xtrain)[sapply(xtrain, is.character)]
for(c in cat_cols){
lbl <- LabelEncoder$new()
lbl$fit(c(xtrain[[c]], xtest[[c]]))
xtrain[[c]] <- lbl$transform(xtrain[[c]])
xtest[[c]] <- lbl$transform(xtest[[c]])
}
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
#> The data contains NA values. Imputing NA with 'NA'
# removing noise column
noise <- c('GrLivArea','TotalBsmtSF')
xtrain[, c(noise) := NULL]
xtest[, c(noise) := NULL]
# fill missing value with -1
xtrain[is.na(xtrain)] <- -1
xtest[is.na(xtest)] <- -1KNN Regression
knn <- KNNTrainer$new(k = 2,prob = T,type = 'reg')
knn$fit(train = xtrain, test = xtest, y = 'SalePrice')
probs <- knn$predict(type = 'prob')
labels <- knn$predict(type='raw')
rmse(actual = xtest$SalePrice, predicted=labels)
#> [1] 49763.34SVM Regression
svm <- SVMTrainer$new()
svm$fit(xtrain, 'SalePrice')
pred <- svm$predict(xtest)
rmse(actual = xtest$SalePrice, predicted = pred)Simple Regresison
lf <- LMTrainer$new(family="gaussian")
lf$fit(X = xtrain, y = "SalePrice")
summary(lf$model)
#>
#> Call:
#> stats::glm(formula = f, family = self$family, data = X, weights = self$weights)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -342200 -14216 -1480 13463 245838
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -2.305e+05 1.632e+06 -0.141 0.887743
#> MSSubClass -1.813e+02 5.370e+01 -3.375 0.000767 ***
#> MSZoning 1.225e+03 1.759e+03 0.696 0.486333
#> LotFrontage -7.683e+00 3.326e+01 -0.231 0.817388
#> LotArea 2.416e-01 1.190e-01 2.031 0.042486 *
#> Street -2.528e+04 1.577e+04 -1.603 0.109193
#> LotShape 2.484e+03 2.084e+03 1.192 0.233505
#> LandContour -3.151e+03 1.966e+03 -1.603 0.109201
#> Utilities -6.564e+04 3.447e+04 -1.904 0.057157 .
#> LotConfig 1.708e+03 1.457e+03 1.173 0.241265
#> LandSlope 1.137e+04 4.738e+03 2.400 0.016564 *
#> Neighborhood 3.162e+02 2.001e+02 1.580 0.114487
#> Condition1 -2.336e+03 7.744e+02 -3.017 0.002625 **
#> Condition2 -1.040e+04 2.956e+03 -3.517 0.000457 ***
#> BldgType -7.246e+02 2.121e+03 -0.342 0.732673
#> HouseStyle 1.270e+03 1.135e+03 1.119 0.263355
#> OverallQual 1.537e+04 1.446e+03 10.630 < 2e-16 ***
#> OverallCond 6.011e+03 1.273e+03 4.720 2.71e-06 ***
#> YearBuilt 4.013e+02 8.225e+01 4.879 1.25e-06 ***
#> YearRemodAdd 1.197e+02 8.127e+01 1.473 0.141139
#> RoofStyle 7.258e+03 2.051e+03 3.539 0.000421 ***
#> RoofMatl -2.095e+04 2.710e+03 -7.732 2.69e-14 ***
#> Exterior1st -2.725e+02 5.223e+02 -0.522 0.602018
#> Exterior2nd 3.827e+02 5.561e+02 0.688 0.491527
#> MasVnrType 3.070e+03 1.659e+03 1.851 0.064529 .
#> MasVnrArea 2.379e+01 7.045e+00 3.377 0.000762 ***
#> ExterQual 2.751e+03 2.423e+03 1.136 0.256450
#> ExterCond 1.855e+03 2.697e+03 0.688 0.491792
#> Foundation -6.744e+02 1.041e+03 -0.648 0.517211
#> BsmtQual 4.414e+03 1.504e+03 2.936 0.003408 **
#> BsmtCond -2.998e+03 2.060e+03 -1.456 0.145804
#> BsmtExposure 5.088e+03 1.009e+03 5.044 5.47e-07 ***
#> BsmtFinType1 -7.513e+02 8.441e+02 -0.890 0.373714
#> BsmtFinSF1 5.249e+00 5.935e+00 0.884 0.376696
#> BsmtFinType2 -2.122e+03 1.443e+03 -1.471 0.141549
#> BsmtFinSF2 1.701e+01 9.997e+00 1.702 0.089119 .
#> BsmtUnfSF 3.625e+00 5.608e+00 0.647 0.518111
#> Heating 4.859e+03 5.226e+03 0.930 0.352707
#> HeatingQC -2.789e+03 1.473e+03 -1.893 0.058691 .
#> CentralAir 9.743e+03 5.777e+03 1.686 0.092059 .
#> Electrical 1.737e+03 1.510e+03 1.150 0.250232
#> `1stFlrSF` 4.560e+01 7.435e+00 6.134 1.26e-09 ***
#> `2ndFlrSF` 5.598e+01 6.169e+00 9.074 < 2e-16 ***
#> LowQualFinSF 4.220e+01 2.107e+01 2.003 0.045452 *
#> BsmtFullBath 9.524e+03 3.083e+03 3.089 0.002064 **
#> BsmtHalfBath 4.079e+03 4.459e+03 0.915 0.360529
#> FullBath 6.131e+03 3.273e+03 1.873 0.061336 .
#> HalfBath -2.286e+03 3.030e+03 -0.754 0.450806
#> BedroomAbvGr -5.536e+03 1.937e+03 -2.858 0.004359 **
#> KitchenAbvGr -1.535e+04 6.145e+03 -2.498 0.012660 *
#> KitchenQual 8.386e+03 1.564e+03 5.363 1.03e-07 ***
#> TotRmsAbvGrd 2.330e+03 1.445e+03 1.613 0.107047
#> Functional -4.940e+03 1.530e+03 -3.229 0.001285 **
#> Fireplaces -1.561e+03 2.673e+03 -0.584 0.559417
#> FireplaceQu 3.874e+03 1.370e+03 2.827 0.004799 **
#> GarageType -1.041e+03 9.860e+02 -1.056 0.291409
#> GarageYrBlt 1.124e+00 5.971e+00 0.188 0.850699
#> GarageFinish 4.476e+02 1.517e+03 0.295 0.767971
#> GarageCars 1.296e+04 3.387e+03 3.827 0.000138 ***
#> GarageArea 1.308e+01 1.145e+01 1.143 0.253427
#> GarageQual 7.405e+03 3.456e+03 2.143 0.032401 *
#> GarageCond -9.136e+02 2.294e+03 -0.398 0.690543
#> PavedDrive -3.015e+03 3.372e+03 -0.894 0.371518
#> WoodDeckSF 2.585e+01 8.821e+00 2.930 0.003469 **
#> OpenPorchSF -7.037e-01 1.730e+01 -0.041 0.967564
#> EnclosedPorch -7.141e+00 2.055e+01 -0.347 0.728358
#> `3SsnPorch` 5.197e+01 4.075e+01 1.275 0.202543
#> ScreenPorch 6.234e+01 1.879e+01 3.317 0.000943 ***
#> PoolArea 3.706e+01 2.669e+01 1.389 0.165266
#> Fence -2.369e+03 1.358e+03 -1.745 0.081312 .
#> MiscVal -7.650e-01 2.015e+00 -0.380 0.704273
#> MoSold -3.008e+02 3.920e+02 -0.767 0.443126
#> YrSold -4.324e+02 8.147e+02 -0.531 0.595781
#> SaleType 2.057e+03 1.339e+03 1.536 0.124885
#> SaleCondition 1.024e+03 1.241e+03 0.826 0.409259
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 1042176816)
#>
#> Null deviance: 6.5520e+12 on 1023 degrees of freedom
#> Residual deviance: 9.8903e+11 on 949 degrees of freedom
#> AIC: 24243
#>
#> Number of Fisher Scoring iterations: 2
predictions <- lf$predict(df = xtest)
rmse(actual = xtest$SalePrice, predicted = predictions)
#> [1] 31444.3Lasso Regression
lf <- LMTrainer$new(family = "gaussian", alpha = 1, lambda = 1000)
lf$fit(X = xtrain, y = "SalePrice")
predictions <- lf$predict(df = xtest)
rmse(actual = xtest$SalePrice, predicted = predictions)
#> [1] 37348.82Ridge Regression
lf <- LMTrainer$new(family = "gaussian", alpha=0)
lf$fit(X = xtrain, y = "SalePrice")
predictions <- lf$predict(df = xtest)
rmse(actual = xtest$SalePrice, predicted = predictions)
#> [1] 37455.88Logistic Regression with CV
lf <- LMTrainer$new(family = "gaussian")
lf$cv_model(X = xtrain, y = 'SalePrice', nfolds = 5, parallel = FALSE)
predictions <- lf$cv_predict(df = xtest)
coefs <- lf$get_importance()
rmse(actual = xtest$SalePrice, predicted = predictions)Random Forest
rf <- RFTrainer$new(n_estimators = 500,classification = 0)
rf$fit(X = xtrain, y = "SalePrice")
pred <- rf$predict(df = xtest)
rf$get_importance()
#> tmp.order.tmp..decreasing...TRUE..
#> OverallQual 806429001574
#> GarageCars 514930089326
#> GarageArea 477243445509
#> 1stFlrSF 432517479568
#> YearBuilt 358248448858
#> FullBath 301425527294
#> KitchenQual 300103186644
#> GarageYrBlt 272675268003
#> 2ndFlrSF 258245739868
#> ExterQual 223237069256
#> BsmtFinSF1 198350632427
#> TotRmsAbvGrd 190770130705
#> YearRemodAdd 172029162481
#> LotArea 168886829326
#> FireplaceQu 167059277652
#> Fireplaces 161457140551
#> MasVnrArea 123525744395
#> BsmtQual 98629991433
#> OpenPorchSF 90468152090
#> LotFrontage 85832302736
#> WoodDeckSF 77688299712
#> HeatingQC 67258379025
#> Foundation 65382098600
#> BsmtUnfSF 63289097509
#> Neighborhood 60544343321
#> BedroomAbvGr 55533166596
#> BsmtFinType1 50990775237
#> GarageType 41772112087
#> Exterior2nd 40330143410
#> BsmtExposure 40104367529
#> MoSold 38855636050
#> MSSubClass 38650931883
#> OverallCond 34833193956
#> HalfBath 32241782664
#> HouseStyle 29979016930
#> Exterior1st 29092430396
#> LotShape 26889365788
#> GarageFinish 26274340608
#> RoofStyle 26212549983
#> YrSold 23276775094
#> BsmtFullBath 20186362425
#> SaleCondition 18449885260
#> PoolArea 17713804419
#> LandContour 16429903396
#> RoofMatl 15416822915
#> ScreenPorch 15244718786
#> MSZoning 14675692653
#> MasVnrType 14550979527
#> LandSlope 12991083252
#> BsmtHalfBath 12809765117
#> LotConfig 12520012881
#> GarageQual 11739965065
#> Fence 11703049011
#> Condition1 10885036153
#> SaleType 10882105335
#> BldgType 10589459949
#> BsmtCond 9917213827
#> BsmtFinSF2 8289027381
#> EnclosedPorch 8152482434
#> GarageCond 7907840713
#> CentralAir 7824960809
#> BsmtFinType2 6972322859
#> Functional 5741981926
#> KitchenAbvGr 5656757289
#> PavedDrive 5094126962
#> ExterCond 4585540342
#> 3SsnPorch 3509837398
#> Electrical 3425486950
#> LowQualFinSF 3282677678
#> Condition2 2945720294
#> Heating 2073545813
#> MiscVal 1488548092
#> Street 348080224
#> Utilities 23425353
rmse(actual = xtest$SalePrice, predicted = pred)
#> [1] 29082.98Xgboost
xgb <- XGBTrainer$new(objective = "reg:linear"
, n_estimators = 500
, eval_metric = "rmse"
, maximize = F
, learning_rate = 0.1
,max_depth = 6)
xgb$fit(X = xtrain, y = "SalePrice", valid = xtest)
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:179201.328125 val-rmse:177621.484375
#> Multiple eval metrics are present. Will use val_rmse for early stopping.
#> Will train until val_rmse hasn't improved in 50 rounds.
#>
#> [51] train-rmse:8308.346680 val-rmse:26317.550781
#> [101] train-rmse:4910.869141 val-rmse:25718.234375
#> [151] train-rmse:3218.866211 val-rmse:25672.822266
#> Stopping. Best iteration:
#> [131] train-rmse:3860.479004 val-rmse:25638.552734
pred <- xgb$predict(xtest)
rmse(actual = xtest$SalePrice, predicted = pred)
#> [1] 25638.56Grid Search
xgb <- XGBTrainer$new(objective ="reg:linear")
gst <-GridSearchCV$new(trainer = xgb,
parameters = list(n_estimators = c(10,50), max_depth = c(5,2)),
n_folds = 3,
scoring = c('accuracy','auc'))
gst$fit(xtrain, "SalePrice")
#> [1] "entering grid search"
#> [1] "In total, 4 models will be trained"
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:144640.171875
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [10] train-rmse:16937.876953
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:138590.734375
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [10] train-rmse:15151.860352
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:144085.812500
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [10] train-rmse:15885.150391
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:144640.171875
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [50] train-rmse:3556.593506
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:138590.734375
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [50] train-rmse:3428.140137
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:144085.812500
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [50] train-rmse:3331.416748
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:145535.093750
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [10] train-rmse:32374.402344
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:139217.687500
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [10] train-rmse:30047.097656
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:144978.218750
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [10] train-rmse:29365.738281
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:145535.093750
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [50] train-rmse:17672.855469
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:139217.687500
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [50] train-rmse:17387.134766
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-rmse:144978.218750
#> Will train until train_rmse hasn't improved in 50 rounds.
#>
#> [50] train-rmse:15668.986328
gst$best_iteration()
#> $n_estimators
#> [1] 10
#>
#> $max_depth
#> [1] 5
#>
#> $accuracy_avg
#> [1] 0
#>
#> $accuracy_sd
#> [1] 0
#>
#> $auc_avg
#> [1] NaN
#>
#> $auc_sd
#> [1] NARandom Search
rf <- RFTrainer$new()
rst <- RandomSearchCV$new(trainer = rf,
parameters = list(n_estimators = c(5,10),
max_depth = c(5,2)),
n_folds = 3,
scoring = c('accuracy','auc'),
n_iter = 3)
rst$fit(xtrain, "SalePrice")
#> [1] "In total, 3 models will be trained"
rst$best_iteration()
#> $n_estimators
#> [1] 10
#>
#> $max_depth
#> [1] 2
#>
#> $accuracy_avg
#> [1] 0.009772447
#>
#> $accuracy_sd
#> [1] 0.004508824
#>
#> $auc_avg
#> [1] NaN
#>
#> $auc_sd
#> [1] NAHere, we will solve a simple binary classification problem (predict people who survived on titanic ship). The idea here is to demonstrate how to use this package to solve classification problems.
Data Preparation
# load class
load('../data/cla_train.rda')
# if the above doesn't work, you can try: load("cla_train.rda")
head(cla_train)
#> PassengerId Survived Pclass
#> 1: 1 0 3
#> 2: 2 1 1
#> 3: 3 1 3
#> 4: 4 1 1
#> 5: 5 0 3
#> 6: 6 0 3
#> Name Sex Age SibSp Parch
#> 1: Braund, Mr. Owen Harris male 22 1 0
#> 2: Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
#> 3: Heikkinen, Miss. Laina female 26 0 0
#> 4: Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
#> 5: Allen, Mr. William Henry male 35 0 0
#> 6: Moran, Mr. James male NA 0 0
#> Ticket Fare Cabin Embarked
#> 1: A/5 21171 7.2500 S
#> 2: PC 17599 71.2833 C85 C
#> 3: STON/O2. 3101282 7.9250 S
#> 4: 113803 53.1000 C123 S
#> 5: 373450 8.0500 S
#> 6: 330877 8.4583 Q
# split the data
split <- createDataPartition(y = cla_train$Survived,p = 0.7)
xtrain <- cla_train[split$Resample1]
xtest <- cla_train[!split$Resample1]
# encode categorical variables - shorter way
for(c in c('Embarked','Sex','Cabin')){
lbl <- LabelEncoder$new()
lbl$fit(c(xtrain[[c]], xtest[[c]]))
xtrain[[c]] <- lbl$transform(xtrain[[c]])
xtest[[c]] <- lbl$transform(xtest[[c]])
}
#> The data contains blank values. Imputing them with 'NA'
#> The data contains blank values. Imputing them with 'NA'
#> The data contains blank values. Imputing them with 'NA'
#> The data contains blank values. Imputing them with 'NA'
#> The data contains blank values. Imputing them with 'NA'
# impute missing values
xtrain[, Age := replace(Age, is.na(Age), median(Age, na.rm = T))]
xtest[, Age := replace(Age, is.na(Age), median(Age, na.rm = T))]
# drop these features
to_drop <- c('PassengerId','Ticket','Name')
xtrain <- xtrain[,-c(to_drop), with=F]
xtest <- xtest[,-c(to_drop), with=F]Now, our data is ready to be served for model training. Let’s do it.
KNN Classification
knn <- KNNTrainer$new(k = 2,prob = T,type = 'class')
knn$fit(train = xtrain, test = xtest, y = 'Survived')
probs <- knn$predict(type = 'prob')
labels <- knn$predict(type='raw')
auc(actual = xtest$Survived, predicted=labels)
#> [1] 0.6385027Naive Bayes Classification
nb <- NBTrainer$new()
nb$fit(xtrain, 'Survived')
pred <- nb$predict(xtest)
#> Warning: predict.naive_bayes(): More features in the newdata are provided as
#> there are probability tables in the object. Calculation is performed based on
#> features to be found in the tables.
auc(actual = xtest$Survived, predicted=pred)
#> [1] 0.7771836SVM Classification
#predicts labels
svm <- SVMTrainer$new()
svm$fit(xtrain, 'Survived')
pred <- svm$predict(xtest)
auc(actual = xtest$Survived, predicted=pred)Logistic Regression
lf <- LMTrainer$new(family="binomial")
lf$fit(X = xtrain, y = "Survived")
summary(lf$model)
#>
#> Call:
#> stats::glm(formula = f, family = self$family, data = X, weights = self$weights)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -2.6102 -0.6018 -0.4367 0.7038 2.4493
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 1.830070 0.616894 2.967 0.00301 **
#> Pclass -0.980785 0.192493 -5.095 3.48e-07 ***
#> Sex 2.508241 0.230374 10.888 < 2e-16 ***
#> Age -0.041034 0.009309 -4.408 1.04e-05 ***
#> SibSp -0.235520 0.117715 -2.001 0.04542 *
#> Parch -0.098742 0.137791 -0.717 0.47361
#> Fare 0.001281 0.002842 0.451 0.65230
#> Cabin 0.008408 0.004786 1.757 0.07899 .
#> Embarked 0.248088 0.166616 1.489 0.13649
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 831.52 on 623 degrees of freedom
#> Residual deviance: 564.76 on 615 degrees of freedom
#> AIC: 582.76
#>
#> Number of Fisher Scoring iterations: 5
predictions <- lf$predict(df = xtest)
auc(actual = xtest$Survived, predicted = predictions)
#> [1] 0.8832145Lasso Logistic Regression
lf <- LMTrainer$new(family="binomial", alpha=1)
lf$cv_model(X = xtrain, y = "Survived", nfolds = 5, parallel = FALSE)
pred <- lf$cv_predict(df = xtest)
auc(actual = xtest$Survived, predicted = pred)Ridge Logistic Regression
lf <- LMTrainer$new(family="binomial", alpha=0)
lf$cv_model(X = xtrain, y = "Survived", nfolds = 5, parallel = FALSE)
pred <- lf$cv_predict(df = xtest)
auc(actual = xtest$Survived, predicted = pred)Random Forest
rf <- RFTrainer$new(n_estimators = 500,classification = 1, max_features = 3)
rf$fit(X = xtrain, y = "Survived")
pred <- rf$predict(df = xtest)
rf$get_importance()
#> tmp.order.tmp..decreasing...TRUE..
#> Sex 67.80128
#> Fare 57.97193
#> Age 48.37045
#> Pclass 24.64915
#> Cabin 21.45972
#> SibSp 13.51637
#> Parch 10.45743
#> Embarked 10.23844
auc(actual = xtest$Survived, predicted = pred)
#> [1] 0.7976827Xgboost
xgb <- XGBTrainer$new(objective = "binary:logistic"
, n_estimators = 500
, eval_metric = "auc"
, maximize = T
, learning_rate = 0.1
,max_depth = 6)
xgb$fit(X = xtrain, y = "Survived", valid = xtest)
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-auc:0.886258 val-auc:0.879085
#> Multiple eval metrics are present. Will use val_auc for early stopping.
#> Will train until val_auc hasn't improved in 50 rounds.
#>
#> [51] train-auc:0.972938 val-auc:0.866370
#> Stopping. Best iteration:
#> [1] train-auc:0.886258 val-auc:0.879085
pred <- xgb$predict(xtest)
auc(actual = xtest$Survived, predicted = pred)
#> [1] 0.879085Grid Search
xgb <- XGBTrainer$new(objective="binary:logistic")
gst <-GridSearchCV$new(trainer = xgb,
parameters = list(n_estimators = c(10,50),
max_depth = c(5,2)),
n_folds = 3,
scoring = c('accuracy','auc'))
gst$fit(xtrain, "Survived")
#> [1] "entering grid search"
#> [1] "In total, 4 models will be trained"
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.144231
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [10] train-error:0.108173
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.134615
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [10] train-error:0.112981
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.115385
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [10] train-error:0.084135
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.144231
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [50] train-error:0.045673
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.134615
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [50] train-error:0.045673
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.115385
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [50] train-error:0.038462
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.211538
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [10] train-error:0.158654
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.201923
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [10] train-error:0.168269
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.206731
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [10] train-error:0.141827
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.211538
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [50] train-error:0.127404
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.201923
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [50] train-error:0.132212
#> converting the data into xgboost format..
#> starting with training...
#> [1] train-error:0.206731
#> Will train until train_error hasn't improved in 50 rounds.
#>
#> [50] train-error:0.108173
gst$best_iteration()
#> $n_estimators
#> [1] 10
#>
#> $max_depth
#> [1] 5
#>
#> $accuracy_avg
#> [1] 0
#>
#> $accuracy_sd
#> [1] 0
#>
#> $auc_avg
#> [1] 0.8619512
#>
#> $auc_sd
#> [1] 0.02280628Random Search
rf <- RFTrainer$new()
rst <- RandomSearchCV$new(trainer = rf,
parameters = list(n_estimators = c(10,50), max_depth = c(5,2)),
n_folds = 3,
scoring = c('accuracy','auc'),
n_iter = 3)
rst$fit(xtrain, "Survived")
#> [1] "In total, 3 models will be trained"
rst$best_iteration()
#> $n_estimators
#> [1] 50
#>
#> $max_depth
#> [1] 5
#>
#> $accuracy_avg
#> [1] 0.7964744
#>
#> $accuracy_sd
#> [1] 0.03090914
#>
#> $auc_avg
#> [1] 0.7729436
#>
#> $auc_sd
#> [1] 0.04283084Let’s create some new feature based on target variable using target encoding and test a model.
# add target encoding features
xtrain[, feat_01 := smoothMean(train_df = xtrain,
test_df = xtest,
colname = "Embarked",
target = "Survived")$train[[2]]]
xtest[, feat_01 := smoothMean(train_df = xtrain,
test_df = xtest,
colname = "Embarked",
target = "Survived")$test[[2]]]
# train a random forest
# Random Forest
rf <- RFTrainer$new(n_estimators = 500,classification = 1, max_features = 4)
rf$fit(X = xtrain, y = "Survived")
pred <- rf$predict(df = xtest)
rf$get_importance()
#> tmp.order.tmp..decreasing...TRUE..
#> Sex 69.787235
#> Fare 60.832089
#> Age 52.982604
#> Pclass 24.419818
#> Cabin 21.419274
#> SibSp 13.112177
#> Parch 10.175269
#> feat_01 6.675399
#> Embarked 6.450819
auc(actual = xtest$Survived, predicted = pred)
#> [1] 0.8018717