Model interpretability and understanding for PyTorch

XAI - An eXplainability toolbox for machine learning

Leave One Feature Out Importance

Features selector based on the self selected-algorithm, loss function and validation method

ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbofan engine.

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

A Julia package for interpretable machine learning with stochastic Shapley values

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

Adding feature_importances_ property to sklearn.cluster.KMeans class

Awesome papers on Feature Selection

Routines and data structures for using isarn-sketches idiomatically in Apache Spark

Variance-based Feature Importance in Neural Networks

CancelOut is a special layer for deep neural networks that can help identify a subset of relevant input features for streaming or static data.

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions i.e. synergy scores.

Official repository of the paper "Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance", M. Carletti, M. Terzi, G. A. Susto.

This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.