plot_interpretable_feature

<p>chr, name of the variable to be plotted</p>

variable

<p>Plot Ceteris Paribus Profile and discretization</p>

Local explanations of machine learning models describe, how features contributed to a single prediction.
This package implements an explanation method based on LIME
(Local Interpretable Model-agnostic Explanations,
see Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>) in which interpretable
inputs are created based on local rather than global behaviour of each original feature.

Przemyslaw Biecek

localModel

LIME-Based Explanations with Interpretable Inputs Based on
Ceteris Paribus Profiles

Mateusz Staniak

Krystian Igras

Alicja Gosiewska

Harel Lustiger

Willy Tadema

plot_interpretable_feature function

Plot Ceteris Paribus Profile and discretization — plot_interpretable_feature

Plot Ceteris Paribus Profile and discretization

plot_interpretable_feature: Plot Ceteris Paribus Profile and discretization

Description

Usage

Arguments

Value