what-if-tool

Model explainer

An interactive tool for exploring and understanding the behavior of machine learning models

Source code/webpage/demos for the What-If Tool

GitHub

928 stars
30 watching
171 forks
Language: HTML
last commit: 4 months ago
Linked from 2 awesome lists

colaboratoryjupyterlab-extensionmachine-learningml-fairnesstensorboardvisualization

Backlinks from these awesome lists:

Related projects:

Repository Description Stars
explainx/explainx A framework to explain and debug blackbox machine learning models with a single line of code. 419
neulab/explainaboard An interactive tool to analyze and compare the performance of natural language processing models 362
modeloriented/dalex A tool to help understand and explain the behavior of complex machine learning models 1,390
modeloriented/modelstudio A tool for creating interactive, model-agnostic explanations of machine learning models in R 328
pair-code/llm-comparator Analyzes LLM responses side-by-side to compare and contrast differences in generated text 347
interpretml/dice Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding. 1,373
thomasp85/lime An R package for providing explanations of predictions made by black box classifiers. 486
marcotcr/anchor Provides a method to generate explanations for predictions made by any black box classifier. 798
giuseppec/iml Provides methods to interpret and explain the behavior of machine learning models 494
modeloriented/ibreakdown A tool for explaining predictions from machine learning models by attributing them to specific input variables and their interactions. 82
tensorflow/model-analysis Evaluates and visualizes the performance of machine learning models. 1,258
eli5-org/eli5 A Python package for debugging and explaining predictions of machine learning classifiers 265
rmarko/explainprediction An R package for explaining the predictions made by machine learning models in data science applications. 2
johnsnowlabs/langtest A tool for testing and evaluating large language models with a focus on AI safety and model assessment. 506
bloomberggraphics/whatiscode Represents Paul Ford's 2015 article on the nature of code in JavaScript. 3,680