kFoldMean {superml}

kFoldMean Calculator

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