bins_tree {autoScorecard}

Automatic Binning Based on Decision Tree Automatic Binning Based on Decision Tree(rpart).

Description

Automatic Binning Based on Decision Tree Automatic Binning Based on Decision Tree(rpart).

Usage

bins_tree(df, key_var, y_var, max_depth = 3, p = 0.1)

Arguments

df

A data.frame with independent variables and target variable.

key_var

A name of index variable name.

y_var

A name of target variable.

max_depth

Set the maximum depth of any node of the final tree, with the root node counted as depth 0. Values greater than 30 rpart will give nonsense results on 32-bit machines.

p

Meet the following conversion formula: minbucket = round(p*nrow(df)).Smallest bucket(rpart):Minimum number of observations in any terminal <leaf> node.

Value

A data frame, including the contents of the bin, the upper bound of the bin, the lower bound of the bin, and all the contents returned by the get_IV function.

Examples

accepts <- read.csv(system.file( "extdata", "accepts.csv", package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
all2 <- bins_tree(df = feature, key_var= "application_id", y_var= "bad_ind"
, max_depth = 3, p = 0.1 )