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
#> <int> <int> <char> <int> <int> <char> <char> <char>
#> 1: 1 60 RL 65 8450 Pave <NA> Reg
#> 2: 2 20 RL 80 9600 Pave <NA> Reg
#> 3: 3 60 RL 68 11250 Pave <NA> IR1
#> 4: 4 70 RL 60 9550 Pave <NA> IR1
#> 5: 5 60 RL 84 14260 Pave <NA> IR1
#> 6: 6 50 RL 85 14115 Pave <NA> IR1
#> LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: Lvl AllPub Inside Gtl CollgCr Norm Norm
#> 2: Lvl AllPub FR2 Gtl Veenker Feedr Norm
#> 3: Lvl AllPub Inside Gtl CollgCr Norm Norm
#> 4: Lvl AllPub Corner Gtl Crawfor Norm Norm
#> 5: Lvl AllPub FR2 Gtl NoRidge Norm Norm
#> 6: Lvl AllPub Inside Gtl Mitchel Norm Norm
#> BldgType HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd RoofStyle
#> <char> <char> <int> <int> <int> <int> <char>
#> 1: 1Fam 2Story 7 5 2003 2003 Gable
#> 2: 1Fam 1Story 6 8 1976 1976 Gable
#> 3: 1Fam 2Story 7 5 2001 2002 Gable
#> 4: 1Fam 2Story 7 5 1915 1970 Gable
#> 5: 1Fam 2Story 8 5 2000 2000 Gable
#> 6: 1Fam 1.5Fin 5 5 1993 1995 Gable
#> RoofMatl Exterior1st Exterior2nd MasVnrType MasVnrArea ExterQual ExterCond
#> <char> <char> <char> <char> <int> <char> <char>
#> 1: CompShg VinylSd VinylSd BrkFace 196 Gd TA
#> 2: CompShg MetalSd MetalSd None 0 TA TA
#> 3: CompShg VinylSd VinylSd BrkFace 162 Gd TA
#> 4: CompShg Wd Sdng Wd Shng None 0 TA TA
#> 5: CompShg VinylSd VinylSd BrkFace 350 Gd TA
#> 6: CompShg VinylSd VinylSd None 0 TA TA
#> Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1
#> <char> <char> <char> <char> <char> <int>
#> 1: PConc Gd TA No GLQ 706
#> 2: CBlock Gd TA Gd ALQ 978
#> 3: PConc Gd TA Mn GLQ 486
#> 4: BrkTil TA Gd No ALQ 216
#> 5: PConc Gd TA Av GLQ 655
#> 6: Wood Gd TA No GLQ 732
#> BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir
#> <char> <int> <int> <int> <char> <char> <char>
#> 1: Unf 0 150 856 GasA Ex Y
#> 2: Unf 0 284 1262 GasA Ex Y
#> 3: Unf 0 434 920 GasA Ex Y
#> 4: Unf 0 540 756 GasA Gd Y
#> 5: Unf 0 490 1145 GasA Ex Y
#> 6: Unf 0 64 796 GasA Ex Y
#> Electrical 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath
#> <char> <int> <int> <int> <int> <int>
#> 1: SBrkr 856 854 0 1710 1
#> 2: SBrkr 1262 0 0 1262 0
#> 3: SBrkr 920 866 0 1786 1
#> 4: SBrkr 961 756 0 1717 1
#> 5: SBrkr 1145 1053 0 2198 1
#> 6: SBrkr 796 566 0 1362 1
#> BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual
#> <int> <int> <int> <int> <int> <char>
#> 1: 0 2 1 3 1 Gd
#> 2: 1 2 0 3 1 TA
#> 3: 0 2 1 3 1 Gd
#> 4: 0 1 0 3 1 Gd
#> 5: 0 2 1 4 1 Gd
#> 6: 0 1 1 1 1 TA
#> TotRmsAbvGrd Functional Fireplaces FireplaceQu GarageType GarageYrBlt
#> <int> <char> <int> <char> <char> <int>
#> 1: 8 Typ 0 <NA> Attchd 2003
#> 2: 6 Typ 1 TA Attchd 1976
#> 3: 6 Typ 1 TA Attchd 2001
#> 4: 7 Typ 1 Gd Detchd 1998
#> 5: 9 Typ 1 TA Attchd 2000
#> 6: 5 Typ 0 <NA> Attchd 1993
#> GarageFinish GarageCars GarageArea GarageQual GarageCond PavedDrive
#> <char> <int> <int> <char> <char> <char>
#> 1: RFn 2 548 TA TA Y
#> 2: RFn 2 460 TA TA Y
#> 3: RFn 2 608 TA TA Y
#> 4: Unf 3 642 TA TA Y
#> 5: RFn 3 836 TA TA Y
#> 6: Unf 2 480 TA TA Y
#> WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea PoolQC
#> <int> <int> <int> <int> <int> <int> <char>
#> 1: 0 61 0 0 0 0 <NA>
#> 2: 298 0 0 0 0 0 <NA>
#> 3: 0 42 0 0 0 0 <NA>
#> 4: 0 35 272 0 0 0 <NA>
#> 5: 192 84 0 0 0 0 <NA>
#> 6: 40 30 0 320 0 0 <NA>
#> Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
#> <char> <char> <int> <int> <int> <char> <char> <int>
#> 1: <NA> <NA> 0 2 2008 WD Normal 208500
#> 2: <NA> <NA> 0 5 2007 WD Normal 181500
#> 3: <NA> <NA> 0 9 2008 WD Normal 223500
#> 4: <NA> <NA> 0 2 2006 WD Abnorml 140000
#> 5: <NA> <NA> 0 12 2008 WD Normal 250000
#> 6: MnPrv Shed 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] 52234.77SVM 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:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 8.944e+05 1.689e+06 0.530 0.596554
#> MSSubClass -1.455e+02 5.032e+01 -2.892 0.003919 **
#> MSZoning -1.113e+03 1.583e+03 -0.703 0.482330
#> LotFrontage -3.261e+01 3.505e+01 -0.930 0.352388
#> LotArea 3.831e-01 1.436e-01 2.668 0.007764 **
#> Street -2.280e+04 1.644e+04 -1.387 0.165699
#> LotShape 6.598e+02 2.209e+03 0.299 0.765231
#> LandContour 4.753e+02 2.130e+03 0.223 0.823471
#> Utilities -5.652e+04 3.608e+04 -1.566 0.117589
#> LotConfig 1.372e+03 1.149e+03 1.194 0.232908
#> LandSlope 1.482e+04 5.102e+03 2.906 0.003748 **
#> Neighborhood -4.745e+02 2.080e+02 -2.281 0.022747 *
#> Condition1 -2.340e+03 9.037e+02 -2.589 0.009767 **
#> Condition2 -1.001e+04 2.893e+03 -3.459 0.000567 ***
#> BldgType 2.449e+02 2.257e+03 0.108 0.913631
#> HouseStyle 4.509e+01 9.958e+02 0.045 0.963896
#> OverallQual 1.428e+04 1.441e+03 9.903 < 2e-16 ***
#> OverallCond 6.695e+03 1.309e+03 5.113 3.84e-07 ***
#> YearBuilt 3.830e+02 8.808e+01 4.349 1.52e-05 ***
#> YearRemodAdd 9.095e+01 8.428e+01 1.079 0.280824
#> RoofStyle 6.199e+03 2.096e+03 2.958 0.003174 **
#> RoofMatl -1.560e+04 2.326e+03 -6.708 3.38e-11 ***
#> Exterior1st -4.137e+02 7.170e+02 -0.577 0.564094
#> Exterior2nd 7.765e+02 6.298e+02 1.233 0.217876
#> MasVnrType 3.406e+03 1.723e+03 1.977 0.048305 *
#> MasVnrArea 2.245e+01 7.580e+00 2.962 0.003130 **
#> ExterQual 7.285e+02 2.571e+03 0.283 0.776970
#> ExterCond 1.254e+03 2.529e+03 0.496 0.620077
#> Foundation -3.760e+03 1.983e+03 -1.896 0.058278 .
#> BsmtQual 8.094e+03 1.789e+03 4.525 6.81e-06 ***
#> BsmtCond -1.099e+03 2.544e+03 -0.432 0.665845
#> BsmtExposure 1.243e+03 1.127e+03 1.103 0.270437
#> BsmtFinType1 -1.561e+03 9.268e+02 -1.685 0.092409 .
#> BsmtFinSF1 8.764e+00 6.632e+00 1.322 0.186650
#> BsmtFinType2 -1.338e+03 1.397e+03 -0.958 0.338329
#> BsmtFinSF2 1.867e+01 9.873e+00 1.891 0.058874 .
#> BsmtUnfSF 2.562e+00 6.476e+00 0.396 0.692462
#> Heating -1.378e+02 3.972e+03 -0.035 0.972333
#> HeatingQC -2.959e+03 1.508e+03 -1.962 0.050054 .
#> CentralAir 5.970e+03 5.599e+03 1.066 0.286565
#> Electrical 1.997e+03 2.182e+03 0.915 0.360373
#> `1stFlrSF` 5.660e+01 7.848e+00 7.212 1.13e-12 ***
#> `2ndFlrSF` 5.686e+01 6.063e+00 9.378 < 2e-16 ***
#> LowQualFinSF 3.005e+01 2.404e+01 1.250 0.211451
#> BsmtFullBath 1.040e+04 3.130e+03 3.324 0.000922 ***
#> BsmtHalfBath 4.035e+03 4.927e+03 0.819 0.413038
#> FullBath 9.275e+03 3.358e+03 2.762 0.005859 **
#> HalfBath -1.313e+03 3.215e+03 -0.408 0.683056
#> BedroomAbvGr -6.794e+03 2.064e+03 -3.291 0.001034 **
#> KitchenAbvGr -2.080e+04 7.048e+03 -2.951 0.003245 **
#> KitchenQual 9.268e+03 1.702e+03 5.446 6.58e-08 ***
#> TotRmsAbvGrd 1.536e+03 1.505e+03 1.021 0.307646
#> Functional -4.123e+03 1.579e+03 -2.611 0.009165 **
#> Fireplaces -8.830e+02 2.784e+03 -0.317 0.751201
#> FireplaceQu 4.331e+03 1.516e+03 2.856 0.004378 **
#> GarageType -7.373e+02 1.304e+03 -0.565 0.571969
#> GarageYrBlt -8.845e+00 5.892e+00 -1.501 0.133621
#> GarageFinish 9.045e+02 1.571e+03 0.576 0.564916
#> GarageCars 1.663e+04 3.630e+03 4.583 5.20e-06 ***
#> GarageArea -8.641e+00 1.170e+01 -0.739 0.460278
#> GarageQual 5.039e+03 4.076e+03 1.236 0.216717
#> GarageCond -4.130e+03 3.990e+03 -1.035 0.300889
#> PavedDrive -2.232e+03 3.608e+03 -0.619 0.536311
#> WoodDeckSF 2.317e+01 9.599e+00 2.413 0.015991 *
#> OpenPorchSF 5.149e+00 1.874e+01 0.275 0.783614
#> EnclosedPorch -4.094e+00 1.910e+01 -0.214 0.830312
#> `3SsnPorch` 3.062e+01 3.466e+01 0.884 0.377084
#> ScreenPorch 4.949e+01 2.121e+01 2.333 0.019862 *
#> PoolArea -3.789e+01 2.823e+01 -1.342 0.179764
#> Fence -2.077e+03 1.398e+03 -1.486 0.137708
#> MiscVal 3.181e+00 3.688e+00 0.863 0.388567
#> MoSold -1.435e+02 4.089e+02 -0.351 0.725680
#> YrSold -9.315e+02 8.418e+02 -1.107 0.268748
#> SaleType 3.539e+03 1.561e+03 2.268 0.023580 *
#> SaleCondition -6.941e+02 1.484e+03 -0.468 0.640153
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 1115076131)
#>
#> Null deviance: 6.6565e+12 on 1023 degrees of freedom
#> Residual deviance: 1.0582e+12 on 949 degrees of freedom
#> AIC: 24312
#>
#> Number of Fisher Scoring iterations: 2
predictions <- lf$predict(df = xtest)
rmse(actual = xtest$SalePrice, predicted = predictions)
#> [1] 28861.82Lasso Regression
lf <- LMTrainer$new(family = "gaussian", alpha = 1, lambda = 1000)
lf$fit(X = xtrain, y = "SalePrice")
predictions <- lf$predict(df = xtest)
rmse(actual = xtest$SalePrice, predicted = predictions)
#> [1] 34580.86Ridge Regression
lf <- LMTrainer$new(family = "gaussian", alpha=0)
lf$fit(X = xtrain, y = "SalePrice")
predictions <- lf$predict(df = xtest)
rmse(actual = xtest$SalePrice, predicted = predictions)
#> [1] 34669.58Logistic Regression with CV
lf <- LMTrainer$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 814703838356
#> GarageCars 508099761498
#> 1stFlrSF 491734847096
#> GarageArea 451112100987
#> YearBuilt 352619308205
#> FullBath 310264362110
#> BsmtFinSF1 299185716539
#> KitchenQual 259901998865
#> GarageYrBlt 248486922461
#> 2ndFlrSF 247859932853
#> TotRmsAbvGrd 190330252732
#> LotArea 177406375972
#> ExterQual 174207748492
#> YearRemodAdd 154261461583
#> Fireplaces 132043881664
#> FireplaceQu 131295258466
#> MasVnrArea 119617322793
#> BsmtQual 113566441087
#> Foundation 104858549549
#> OpenPorchSF 103666794521
#> LotFrontage 94344420430
#> BsmtUnfSF 74477805402
#> Neighborhood 73691766244
#> BsmtFinType1 73639055397
#> WoodDeckSF 66183677474
#> BedroomAbvGr 59485192985
#> GarageType 52019228588
#> HeatingQC 51010976404
#> Exterior2nd 44813371442
#> RoofStyle 41276355532
#> MoSold 37891677345
#> MSSubClass 36326975984
#> OverallCond 35379904577
#> HouseStyle 33748494732
#> BsmtFullBath 31504803491
#> HalfBath 30594380307
#> Exterior1st 30012907766
#> GarageFinish 26533940995
#> BsmtExposure 22679562631
#> SaleCondition 22373273806
#> LotShape 20682865510
#> PoolArea 19708390441
#> MSZoning 19475426262
#> YrSold 19240548414
#> CentralAir 17744449654
#> ScreenPorch 16424157765
#> LotConfig 16421910403
#> MasVnrType 16011429349
#> SaleType 15582285428
#> BsmtHalfBath 14732371130
#> LandContour 14474438364
#> BldgType 13815125878
#> RoofMatl 13760138829
#> BsmtFinSF2 13604724855
#> Fence 13454832727
#> LandSlope 12332196861
#> Condition1 11120609995
#> GarageCond 10972277588
#> EnclosedPorch 9643303512
#> GarageQual 9415428695
#> BsmtCond 9382636762
#> BsmtFinType2 8533999374
#> Functional 6159744474
#> PavedDrive 5450372868
#> ExterCond 5352738731
#> KitchenAbvGr 5153125554
#> Electrical 3977319783
#> Condition2 2664097220
#> Heating 2321597300
#> 3SsnPorch 2036587090
#> MiscVal 1623603919
#> LowQualFinSF 1090567307
#> Street 541659531
#> Utilities 21305800
rmse(actual = xtest$SalePrice, predicted = pred)
#> [1] 25446.54Xgboost
xgb <- XGBTrainer$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] 0.01269642
#>
#> $accuracy_sd
#> [1] 0.001703672
#>
#> $auc_avg
#> [1] NaN
#>
#> $auc_sd
#> [1] NAHere, we will solve a simple binary classification problem (predict people who survived on titanic ship). The idea here is to demonstrate how to use this package to solve classification problems.
Data Preparation
# load class
load('../data/cla_train.rda')
# if the above doesn't work, you can try: load("cla_train.rda")
head(cla_train)
#> PassengerId Survived Pclass
#> <int> <int> <int>
#> 1: 1 0 3
#> 2: 2 1 1
#> 3: 3 1 3
#> 4: 4 1 1
#> 5: 5 0 3
#> 6: 6 0 3
#> Name Sex Age SibSp Parch
#> <char> <char> <num> <int> <int>
#> 1: Braund, Mr. Owen Harris male 22 1 0
#> 2: Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
#> 3: Heikkinen, Miss. Laina female 26 0 0
#> 4: Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
#> 5: Allen, Mr. William Henry male 35 0 0
#> 6: Moran, Mr. James male NA 0 0
#> Ticket Fare Cabin Embarked
#> <char> <num> <char> <char>
#> 1: A/5 21171 7.2500 S
#> 2: PC 17599 71.2833 C85 C
#> 3: STON/O2. 3101282 7.9250 S
#> 4: 113803 53.1000 C123 S
#> 5: 373450 8.0500 S
#> 6: 330877 8.4583 Q
# split the data
split <- createDataPartition(y = cla_train$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 69.10742
#> Fare 57.96084
#> Age 48.50156
#> Pclass 23.91175
#> Cabin 21.19329
#> SibSp 12.58503
#> Parch 10.55128
#> Embarked 10.07059
auc(actual = xtest$Survived, predicted = pred)
#> [1] 0.7988414Xgboost
xgb <- XGBTrainer$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] 0.8028846
#>
#> $accuracy_sd
#> [1] 0.01733438
#>
#> $auc_avg
#> [1] 0.7804264
#>
#> $auc_sd
#> [1] 0.02631447Let’s create some new feature based on target variable using target encoding and test a model.
# add target encoding features
xtrain[, feat_01 := smoothMean(train_df = xtrain,
test_df = xtest,
colname = "Embarked",
target = "Survived")$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$get_importance()
#> tmp.order.tmp..decreasing...TRUE..
#> Sex 71.417138
#> Fare 61.039958
#> Age 51.787990
#> Pclass 24.257112
#> Cabin 21.549374
#> SibSp 12.374317
#> Parch 10.392826
#> feat_01 6.490151
#> Embarked 6.270997
auc(actual = xtest$Survived, predicted = pred)
#> [1] 0.7988414