A game theoretic approach to explain the output of any machine learning model.
- interpretability
- machine-learning
- deep-learning
- gradient-boosting
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A game theoretic approach to explain the output of any machine learning model.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Lime: Explaining the predictions of any machine learning classifier
Learning Convolutional Neural Networks with Interactive Visualization.
Fit interpretable models. Explain blackbox machine learning.
Debugging, monitoring and visualization for Python Machine Learning and Data Science
Topic in, narrated explainer video out. A Claude Code / Codex skill that turns any topic into a black-canvas motion-graphics explainer video with TTS voiceover, subtitles and a chapter progress bar. Chinese or English; every frame drawn in code with Remotion.
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Algorithms for explaining machine learning models
Interpretability and explainability of data and machine learning models
moDel Agnostic Language for Exploration and eXplanation
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
TranslatedA skill for making Douyin hit "Vibe knowledge showcase" educational short films: deeply breaks down viral videos, replicates the art style, produces a director's storyboard table, adds sound effects automatically, and outputs the finished video in one click
Python package for stacking (machine learning technique)
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
Frametell: turn your ideas and material into vivid explainer videos with an AI director agent. Local-first, any model.
TranslatedVibe Knowledge Showcase (Vibe 知识大赏): one knowledge point → one premium short knowledge video with narration and a cited source for every sentence (Claude Code skill · 44 styles · frame-by-frame video rendered with code)
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University
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Code for the experiment proposed in Section 4.2 of the paper "The Bouncer Problem: Challenges to Remote Explainability".