ice_cal: Function to calculate Individual Conditional Expectation...

In thiyangt/explainer: Machine Learning Interpretability Tools

Description

Function to create partial dependence plots given features and the training set

Usage

ice_cal(model, variable, fulldf, subsetdf, grid.resolution = 10,
  trim.outliers = FALSE)

Arguments

model	name of the randomForest model
variable	name of the interested feature
fulldf	full data set used to fit the random forest
subsetdf	subset of the dataset use to compute ice
grid.resolution	number of levels to be considered in the interested feature
trim.outliers	if TRUE range for the grid.resolution is computed trimming the outliers

Value

a dataframe including corresponding probability for each class

Author(s)

Thiyanga Talagala

thiyangt/explainer documentation built on July 20, 2019, 6:58 p.m.