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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")
# superml::check_package("caret")
library(data.table)
library(caret)
#> Loading required package: ggplot2
#> Loading required package: lattice
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] 50619.75SVM 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)
#>
#> Coefficients: (1 not defined because of singularities)
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 1.296e+06 1.605e+06 0.807 0.419765
#> MSSubClass -1.596e+02 4.528e+01 -3.524 0.000445 ***
#> MSZoning -2.418e+03 1.744e+03 -1.387 0.165864
#> LotFrontage -5.934e+01 3.400e+01 -1.745 0.081257 .
#> LotArea 4.490e-01 1.366e-01 3.287 0.001051 **
#> Street -4.059e+04 2.088e+04 -1.944 0.052238 .
#> LotShape 8.265e+01 2.098e+03 0.039 0.968591
#> LandContour -1.059e+03 2.272e+03 -0.466 0.641316
#> Utilities NA NA NA NA
#> LotConfig 1.520e+03 1.092e+03 1.392 0.164303
#> LandSlope 1.006e+04 5.030e+03 2.001 0.045718 *
#> Neighborhood 5.015e+02 1.987e+02 2.524 0.011761 *
#> Condition1 -1.763e+03 9.432e+02 -1.870 0.061859 .
#> Condition2 -9.393e+03 2.832e+03 -3.317 0.000944 ***
#> BldgType -6.226e+02 1.974e+03 -0.315 0.752598
#> HouseStyle 1.973e+02 8.855e+02 0.223 0.823775
#> OverallQual 1.680e+04 1.364e+03 12.316 < 2e-16 ***
#> OverallCond 5.159e+03 1.225e+03 4.211 2.79e-05 ***
#> YearBuilt 2.392e+02 8.954e+01 2.672 0.007678 **
#> YearRemodAdd 1.355e+02 8.129e+01 1.667 0.095914 .
#> RoofStyle 5.727e+03 1.921e+03 2.981 0.002945 **
#> RoofMatl -1.288e+04 2.533e+03 -5.083 4.47e-07 ***
#> Exterior1st -1.895e+03 6.889e+02 -2.750 0.006069 **
#> Exterior2nd 1.246e+03 6.551e+02 1.902 0.057430 .
#> MasVnrType 3.984e+03 1.695e+03 2.350 0.018986 *
#> MasVnrArea 3.162e+01 7.220e+00 4.379 1.32e-05 ***
#> ExterQual 2.974e+03 2.364e+03 1.258 0.208725
#> ExterCond 2.453e+03 2.420e+03 1.014 0.310879
#> Foundation -2.473e+03 1.525e+03 -1.622 0.105184
#> BsmtQual 1.493e+03 1.488e+03 1.004 0.315833
#> BsmtCond -4.395e+02 1.453e+03 -0.302 0.762416
#> BsmtExposure 2.758e+03 9.849e+02 2.800 0.005211 **
#> BsmtFinType1 -1.829e+03 8.187e+02 -2.235 0.025668 *
#> BsmtFinSF1 7.017e+00 6.055e+00 1.159 0.246795
#> BsmtFinType2 -1.110e+03 1.219e+03 -0.911 0.362676
#> BsmtFinSF2 1.368e+01 9.628e+00 1.421 0.155567
#> BsmtUnfSF 1.118e+00 5.842e+00 0.191 0.848287
#> Heating -1.301e+03 3.584e+03 -0.363 0.716799
#> HeatingQC -2.912e+03 1.404e+03 -2.074 0.038313 *
#> CentralAir 5.515e+03 5.488e+03 1.005 0.315212
#> Electrical 1.643e+03 1.523e+03 1.079 0.280997
#> `1stFlrSF` 3.679e+01 7.274e+00 5.058 5.09e-07 ***
#> `2ndFlrSF` 3.062e+01 6.024e+00 5.083 4.47e-07 ***
#> LowQualFinSF 2.060e+01 2.313e+01 0.891 0.373282
#> BsmtFullBath 1.058e+04 3.048e+03 3.470 0.000544 ***
#> BsmtHalfBath -1.596e+03 4.871e+03 -0.328 0.743172
#> FullBath 9.015e+03 3.216e+03 2.804 0.005156 **
#> HalfBath 3.567e+03 2.997e+03 1.190 0.234208
#> BedroomAbvGr -5.661e+03 1.943e+03 -2.913 0.003666 **
#> KitchenAbvGr -1.355e+04 6.414e+03 -2.112 0.034914 *
#> KitchenQual 6.223e+03 1.796e+03 3.465 0.000554 ***
#> TotRmsAbvGrd 5.676e+03 1.455e+03 3.902 0.000102 ***
#> Functional -4.137e+03 1.583e+03 -2.614 0.009080 **
#> Fireplaces -1.663e+02 2.554e+03 -0.065 0.948105
#> FireplaceQu 4.029e+03 1.404e+03 2.870 0.004191 **
#> GarageType 1.664e+02 1.380e+03 0.121 0.904026
#> GarageYrBlt 1.581e+00 5.738e+00 0.276 0.782978
#> GarageFinish -1.441e+01 1.525e+03 -0.009 0.992463
#> GarageCars 1.323e+04 3.501e+03 3.780 0.000167 ***
#> GarageArea 1.683e+01 1.183e+01 1.423 0.155049
#> GarageQual 8.273e+03 3.293e+03 2.513 0.012149 *
#> GarageCond -1.480e+03 2.165e+03 -0.684 0.494158
#> PavedDrive -2.902e+03 3.090e+03 -0.939 0.347968
#> WoodDeckSF 2.260e+01 9.139e+00 2.473 0.013569 *
#> OpenPorchSF 2.219e+01 1.829e+01 1.213 0.225405
#> EnclosedPorch 2.817e+01 1.862e+01 1.513 0.130725
#> `3SsnPorch` 5.053e+01 3.686e+01 1.371 0.170751
#> ScreenPorch 8.503e+01 2.091e+01 4.067 5.15e-05 ***
#> PoolArea -8.682e+01 2.844e+01 -3.052 0.002334 **
#> Fence -9.121e+02 1.421e+03 -0.642 0.520980
#> MiscVal 2.612e-01 1.844e+00 0.142 0.887346
#> MoSold -2.057e+02 3.821e+02 -0.538 0.590532
#> YrSold -1.056e+03 7.972e+02 -1.324 0.185735
#> SaleType 3.283e+03 1.351e+03 2.431 0.015250 *
#> SaleCondition 1.627e+03 1.265e+03 1.286 0.198766
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 1025713223)
#>
#> Null deviance: 6.2770e+12 on 1023 degrees of freedom
#> Residual deviance: 9.7443e+11 on 950 degrees of freedom
#> AIC: 24226
#>
#> Number of Fisher Scoring iterations: 2
predictions <- lf$predict(df = xtest)
rmse(actual = xtest$SalePrice, predicted = predictions)
#> [1] 36414.68Lasso Regression
lf <- LMTrainer$new(family = "gaussian", alpha = 1, lambda = 1000)
lf$fit(X = xtrain, y = "SalePrice")
predictions <- lf$predict(df = xtest)
rmse(actual = xtest$SalePrice, predicted = predictions)
#> [1] 40647.6Ridge Regression
lf <- LMTrainer$new(family = "gaussian", alpha=0)
lf$fit(X = xtrain, y = "SalePrice")
predictions <- lf$predict(df = xtest)
rmse(actual = xtest$SalePrice, predicted = predictions)
#> [1] 41033.56Logistic Regression with CV
lf <- LMTrainer$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 805396946851
#> GarageCars 522277512570
#> GarageArea 492504056036
#> 1stFlrSF 456146530362
#> YearBuilt 321001356223
#> FullBath 273711059272
#> BsmtFinSF1 256640207793
#> GarageYrBlt 242955028927
#> BsmtQual 221599078735
#> TotRmsAbvGrd 197896142442
#> LotArea 187832123674
#> ExterQual 172679747974
#> 2ndFlrSF 165575198771
#> YearRemodAdd 153466157731
#> FireplaceQu 138693929431
#> MasVnrArea 128673493393
#> KitchenQual 117443303312
#> Fireplaces 111705942290
#> OpenPorchSF 92631484839
#> Foundation 83286121952
#> LotFrontage 78827754312
#> BsmtUnfSF 65668598298
#> BsmtFinType1 61625988292
#> WoodDeckSF 57948560794
#> Neighborhood 57616557231
#> HeatingQC 47085794011
#> BedroomAbvGr 44560418156
#> GarageType 41061811034
#> MSSubClass 38186219440
#> OverallCond 32843017602
#> Exterior2nd 32477077925
#> HalfBath 31853557276
#> MoSold 31439094626
#> Exterior1st 27829430593
#> HouseStyle 27529302283
#> YrSold 26883223060
#> RoofStyle 26445458267
#> BsmtFullBath 25895731346
#> LotShape 22357815068
#> BsmtExposure 21438623304
#> GarageFinish 20395336117
#> MSZoning 17500907349
#> SaleCondition 17274027042
#> MasVnrType 17169758549
#> ScreenPorch 16281657555
#> GarageQual 15715484041
#> LandContour 15003410671
#> CentralAir 14567933496
#> LotConfig 14394740952
#> BldgType 13477548912
#> GarageCond 13395753051
#> SaleType 13189102573
#> EnclosedPorch 10687715135
#> BsmtFinSF2 10438619040
#> Condition1 10308218603
#> LandSlope 9906284042
#> ExterCond 8994393632
#> Functional 7360746298
#> BsmtFinType2 6937544624
#> RoofMatl 6765618719
#> KitchenAbvGr 6604190117
#> BsmtCond 6435711990
#> Fence 6259391880
#> LowQualFinSF 5518554748
#> PavedDrive 4503508900
#> Heating 3676274614
#> Condition2 3363484182
#> BsmtHalfBath 2930090851
#> Electrical 2626782717
#> 3SsnPorch 2014013123
#> MiscVal 1804559185
#> PoolArea 1794636554
#> Street 317976991
#> Utilities 0
rmse(actual = xtest$SalePrice, predicted = pred)
#> [1] 33814.05Xgboost
xgb <- XGBTrainer$new(objective = "reg:linear"
, n_estimators = 500
, eval_metric = "rmse"
, maximize = F
, learning_rate = 0.1
,max_depth = 6)
xgb$fit(X = xtrain, y = "SalePrice", valid = xtest)
pred <- xgb$predict(xtest)
rmse(actual = xtest$SalePrice, predicted = pred)Grid Search
xgb <- XGBTrainer$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")
gst$best_iteration()Random Search
rf <- RFTrainer$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] 5
#>
#> $max_depth
#> [1] 5
#>
#> $accuracy_avg
#> [1] 0.005850878
#>
#> $accuracy_sd
#> [1] 0.002902616
#>
#> $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)
#>
#> 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$fit(X = xtrain, y = "Survived", valid = xtest)
pred <- xgb$predict(xtest)
auc(actual = xtest$Survived, predicted = pred)Grid Search
xgb <- XGBTrainer$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")
gst$best_iteration()Random Search
rf <- RFTrainer$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] 0.8018717These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.