Explainable Machine Learning in Survival Analysis
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Updated
Jun 15, 2024 - R
Explainable Machine Learning in Survival Analysis
Fit interpretable models. Explain blackbox machine learning.
A curated list of awesome responsible machine learning resources.
Robustness of Global Feature Effect Explanations (ECML PKDD 2024)
Interpretable Configurational Regression: An R Package
An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization
FeatureMAP (Feature-preserving Manifold Approximation and Projection) is an interpratable dimensionality reduction tool.
Code for paper "Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement", Neurips 2023
moDel Agnostic Language for Exploration and eXplanation
A Textbook Remedy for Domain Shifts Knowledge Priors for Medical Image Analysis
[HELP REQUESTED] Generalized Additive Models in Python
PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
Exploring and Modeling High-Dimensional Data
[CVPR 2024] CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
Implementation of LIME focused on producing user-centric local explanations for image classifiers.
Efficient R implementation of SHAP
Local interpretability for survival models
Self-Supervised Adaptive and Interpretable Anomaly Detection with Dynamic Operating Limits
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.
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