feature-importance

Model interpretability and understanding for PyTorch

#计算机科学#XAI - An eXplainability toolbox for machine learning

#计算机科学#Leave One Feature Out Importance

#计算机科学#Features selector based on the self selected-algorithm, loss function and validation method

#计算机科学#Shapley Interactions and Shapley Values for Machine Learning

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) m...

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...

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

#计算机科学#Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

#计算机科学#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

#计算机科学#Beta Machine Learning Toolkit

A Julia package for interpretable machine learning with stochastic Shapley values

#计算机科学#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#Awesome papers on Feature Selection

#数据仓库#Routines and data structures for using isarn-sketches idiomatically in Apache Spark

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

#计算机科学#Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

#计算机科学#Variance-based Feature Importance in Neural Networks

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