kFoldMean: kFoldMean Calculator

In ssi-ashraf/superml: Build Machine Learning Models Like Using Python's Scikit-Learn Library in R

Description

Calculates out-of-fold mean features (also known as target encoding) for train and test data. This strategy is widely used to avoid overfitting or causing leakage while creating features using the target variable. This method is experimental. If the results you get are unexpected, please report them in github issues.

Usage

kFoldMean(train_df, test_df, colname, target, n_fold = 5, seed = 42)

Arguments

train_df	train dataset
test_df	test dataset
colname	name of categorical column
target	the target or dependent variable, should be a string.
n_fold	the number of folds to use for doing kfold computation, default=5
seed	the seed value, to ensure reproducibility, it could be any positive value, default=42

Value

a train and test data table with out-of-fold mean value of the target for the given categorical variable

Examples

train <- data.frame(region=c('del','csk','rcb','del','csk','pune','guj','del'),
                    win = c(0,1,1,0,0,0,0,1))
test <- data.frame(region=c('rcb','csk','rcb','del','guj','pune','csk','kol'))
train_result <- kFoldMean(train_df = train,
                          test_df = test,
                          colname = 'region',
                          target = 'win',
                          seed = 1220)$train

test_result <- kFoldMean(train_df = train,
                         test_df = test,
                         colname = 'region',
                         target = 'win',
                         seed = 1220)$test

ssi-ashraf/superml documentation built on Nov. 5, 2019, 9:18 a.m.