DrWhy.AI - the collection of tools for Visual Exploration, Explanation and Debugging of Predictive Models

It takes a village to raise a ~~child~~ model.

The way how we do predictive modeling is very ineffective. We spend way too much time on manual time consuming and easy to automate activities like data cleaning and exploration, crisp modeling, model validation. Instead of focusing on model understanding, productisation and communication.

Here we gather tools that can be use to make out work more efficient through the whole model lifecycle. The unified grammar beyond DrWhy.AI universe is described in the Predictive Models: Visual Exploration, Explanation and Debugging book.

Lifecycle for Predictive Models

The DrWhy is based on an unified Model Development Process based on RUP. Find an overview in the diagram below.

Tools that are usefull during the model lifetime. stands for our internal tools.

1. Data preparation

dataMaid; A Suite of Checks for Identification of Potential Errors in a Data Frame as Part of the Data Screening Process
inspectdf; A collection of utilities for columnwise summary, comparison and visualisation of data frames.
validate; Professional data validation for the R environment
errorlocate; Find and replace erroneous fields in data using validation rules
ggplot2; System for declaratively creating graphics, based on The Grammar of Graphics.

2. Data understanding

Model Agnostic Variable Importance Scores. Surrogate learning = Train an elastic model and measure feature importance in such model. See DALEX, Model Class Reliance MCR
vip Variable importance plots
SAFE Surrogate learning = Train an elastic model and extract feature transformations.
xspliner Using surrogate black-boxes to train interpretable spline based additive models
factorMerger Set of tools for factors merging paper
ingredients Set of tools for model level feature effects and feature importance.

4. Model assembly

mlr Machine Learning in R paper
caret Classification And Regression Training paper

5. Model audit

auditor model verification, validation, and error analysis vigniette
DALEX Descriptive mAchine Learning EXplanations
iml; interpretable machine learning R package
randomForestExplainer A set of tools to understand what is happening inside a Random Forest
survxai Explanations for survival models paper

6. Model delivery

breakDown, pyBreakDown and breakDown2 Model Agnostic Explainers for Individual Predictions (with interactions)
ceterisParibus, pyCeterisParibus, ceterisParibusD3 and ceterisParibus2 Ceteris Paribus Plots (What-If plots) for explanations of a single observation
localModel and live LIME-like explanations with interpretable features based on Ceteris Paribus curves.
lime; Local Interpretable Model-Agnostic Explanations (R port of original Python package)
shapper An R wrapper of SHAP python library
modelDown modelDown generates a website with HTML summaries for predictive models
drifter Concept Drift and Concept Shift Detection for Predictive Models
archivist A set of tools for datasets and plots archiving paper