smoothMean: smoothMean Calculator

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

Description

Calculates target encodings using a smoothing parameter and count of categorical variables. This approach is more robust to possibility of leakage and avoid overfitting.

Usage

smoothMean(train_df, test_df, colname, target, min_samples_leaf = 1,
  smoothing = 1, noise_level = 0)

Arguments

train_df	train dataset
test_df	test dataset
colname	name of categorical column
target	name of target column
min_samples_leaf	minimum samples to take category average into account
smoothing	smoothing effect to balance categorical average vs prior
noise_level	random noise to add, optional

Value

a train and test data table with mean encodings of the target for the given categorical variable

Examples

train <- data.frame(region=c('del','csk','rcb','del','csk','pune','guj','del'),
                    win = c(0,1,1,0,0,1,0,1))
test <- data.frame(region=c('rcb','csk','rcb','del','guj','pune','csk','kol'))

# calculate encodings
all_means <- smoothMean(train_df = train,
                         test_df = test,
                         colname = 'region',
                         target = 'win')
train_mean <- all_means$train
test_mean <- all_means$test

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