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A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.
Simplicial Homology Global Optimization
This repository was created as an implementation approach for a project on "Watermarking Deep Neural Networks".
Tiny Tutorial on https://arxiv.org/abs/1703.04730
Generative Adversarial Network (GAN) that can produce tabular samples given datasets, and build a general generative model that receives a black-box as a discriminator and can still generate samples from the tabular data.
Using LIME and SHAP for model interpretability of Machine Learning Black-box models.
Getting explanations for predictions made by black box models.
Interpreting Categorical Data Classifiers using Explanation-based Locality
A Global Model-Agnostic Rule-Based XAI Method based on Parameterised Event Primitives for Time Series Classifiers