Fit interpretable models. Explain blackbox machine learning.
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Updated
Aug 20, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
Evaluation and Tracking for LLM Experiments
Interpretable Machine Learning via Rule Extraction
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
A curated list of awesome responsible machine learning resources.
👋 Xplique is a Neural Networks Explainability Toolbox
Explainable Artificial Intelligence through Contextual Importance and Utility
An Open-Source Library for the interpretability of time series classifiers
Automating machine learning training and save an SQL version of the model
Fast and explainable clustering in Python
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
Rough set and machine learning data structures, algorithms and tools, including algorithms for discernibility matrix, reducts, decision rules, classification (RoughSet, KNN, RIONIDA, AQ15, C4.5, SVM, NeuralNetwork and many others), discretization (1R, Entropy Minimization, ChiMerge, MD), and tool for interactive and explainable machine learning.
Explain and train language models that extract information from long medical documents with the Masked Sampling Procedure (MSP)
Data-driven discrimination in relational classification
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
A list of (post-hoc) XAI for time series
XMLX GitHub configuration
OmniXAI: A Library for eXplainable AI
A novel Inductive Logic Programming(ILP) system based on Meta Inverse Entailment in Python.
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