ModelOriented/randomForestExplainer

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R
Created over 7 years ago
Updated 8 months ago

ModelOriented/randomForestExplainer

ModelOriented

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A set of tools to understand what is happening inside a Random Forest

randomForestExplainer

A set of tools to understand what is happening inside a Random Forest. A detailed discussion of the package and importance measures it implements can be found here: Master thesis on randomForestExplainer.

Installation

# the easiest way to get randomForestExplainer is to install it from CRAN:
install.packages("randomForestExplainer")

# Or the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("ModelOriented/randomForestExplainer")

Vignette

Understanding random forests with randomForestExplainer

Cheatsheets

A one-page summary

Examples

Initial vignette for glioblastoma data

DALEX

moDel Agnostic Language for Exploration and eXplanation

DrWhy

DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

modelStudio

📍 Interactive Studio for Explanatory Model Analysis

modelDown

modelDown generates a website with HTML summaries for predictive models

forester

Trees are all you need

survex

Explainable Machine Learning in Survival Analysis

fairmodels

Flexible tool for bias detection, visualization, and mitigation

iBreakDown

Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)

treeshap

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

shapviz

SHAP Plots in R

DALEXtra

Extensions for the DALEX package

auditor

Model verification, validation, and error analysis

shapper

An R wrapper of SHAP python library

ingredients

Effects and Importances of Model Ingredients

SAFE

Surrogate Assisted Feature Extraction

kernelshap

Different SHAP algorithms

DALEX-docs

Documentation for the DALEX project

Jupyter Notebook

live

Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN

ArenaR

Data generator for Arena - interactive XAI dashboard

rSAFE

Surrogate Assisted Feature Extraction in R

EIX

Structure mining for xgboost model

factorMerger

Set of tools to support results from post hoc testing

EloML

R package EloML: Elo rating system for machine learning models

EMMA

Evaluation of Methods for dealing with Missing data in Machine Learning algorithms

xspliner

Explain black box with GLM

hstats

Friedman's H-statistics

Arena

Interactive XAI dashboard

MAIR

Monitoring of AI Regulations

pyCeterisParibus

Python library for Ceteris Paribus Plots (What-if plots)

drifter

Concept Drift and Concept Shift Detection for Predictive Models

xai2shiny

Create Shiny application with model exploration from explainers

localModel

LIME-like explanations with interpretable features based on Ceteris Paribus curves. Now on CRAN.

vivo

Variable importance via oscillations

corrgrapher

Visualize correlations between variables

metaMIMIC

Jupyter Notebook

EvidenceBasedML

Evidence-Based Machine Learning

weles

triplot

Triplot: Instance- and data-level explanations for the groups of correlated features.

xai2cloud

Create web API from model explainers

xaibot

XAI chat bot for Titanic model - created with plumber

FairPAN

AI-strategies-papers-regulations-monitoring

Monitoring of AI strategies, papers, and regulations

Jupyter Notebook

piBreakDown

python version of iBreakDown

RME

Recurrent Memory Explainer

mogger

Logger for Predictive Models

ceterisParibus2

Very experimental version of the ceterisParibus package.

Jupyter Notebook

DrWhyTemplate

shimex

R Package for Exploring Models with Shiny App

DALEX2

Explain! Package with core wrappers for DrWhy universe.

ModelDevelopmentProcess

Source codes for Model Development Process plots

Hex4DrWhy

Shiny app for logo prototyping