Fit interpretable models. Explain blackbox machine learning.
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Updated
Jun 1, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
Implementation of LIME focused on producing user-centric local explanations for image classifiers.
Efficient R implementation of SHAP
A curated list of awesome responsible machine learning resources.
FeatureMAP (Feature-preserving Manifold Approximation and Projection) is an interpratable dimensionality reduction tool.
Local interpretability for survival models
Self-Supervised Adaptive and Interpretable Anomaly Detection with Dynamic Operating Limits
A Textbook Remedy for Domain Shifts Knowledge Priors for Medical Image Analysis
moDel Agnostic Language for Exploration and eXplanation
[HELP REQUESTED] Generalized Additive Models in Python
This is a Python library that implements a Multi-objective Symbolic Regression algorithm. It can be used as a Machine Learning algorithm to create predictive models in the form of mathematical expressions.
Interpretable Multi Agent Reinforcement Learning with a Quality DIversity Approach
Official Implementation of the paper guided attention for interpretable motion captioning
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
counterfactuals: An R package for Counterfactual Explanation Methods
Robust regression algorithm that can be used for explaining black box models (Python implementation)
AutoML system for building trustworthy peptide bioactivity predictors
PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
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