feature-importance
There are 225 repositories under feature-importance topic.
pytorch/captum
Model interpretability and understanding for PyTorch
EthicalML/xai
XAI - An eXplainability toolbox for machine learning
aerdem4/lofo-importance
Leave One Feature Out Importance
duxuhao/Feature-Selection
Features selector based on the self selected-algorithm, loss function and validation method
kochlisGit/ProphitBet-Soccer-Bets-Predictor
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.
dominance-analysis/dominance-analysis
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.
archd3sai/Predictive-Maintenance-of-Aircraft-Engine
In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbofan engine.
csinva/hierarchical-dnn-interpretations
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" š§ (ICLR 2019)
laura-rieger/deep-explanation-penalization
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
sylvaticus/BetaML.jl
Beta Machine Learning Toolkit
nredell/ShapML.jl
A Julia package for interpretable machine learning with stochastic Shapley values
nredell/shapFlex
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
YousefGh/kmeans-feature-importance
Adding feature_importances_ property to sklearn.cluster.KMeans class
mlpapers/feature-selection
Awesome papers on Feature Selection
isarn/isarn-sketches-spark
Routines and data structures for using isarn-sketches idiomatically in Apache Spark
rebelosa/feature-importance-neural-networks
Variance-based Feature Importance in Neural Networks
unnir/CancelOut
CancelOut is a special layer for deep neural networks that can help identify a subset of relevant input features for streaming or static data.
csinva/disentangled-attribution-curves
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
mmschlk/shapiq
SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions i.e. synergy scores.
mattiacarletti/DIFFI
Official repository of the paper "Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance", M. Carletti, M. Terzi, G. A. Susto.
JonathanCrabbe/Simplex
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'.
teyang-lau/HDB_Resale_Prices
Predicted and identified the drivers of Singapore HDB resale prices (2015-2019) with 0.96 Rsquare & $20,000 MAE. Web app deployment using Streamlit for user price prediction.
CederGroupHub/s4
Solid-state synthesis science analyzer. Thermo, features, ML, and more.
statmlben/dnn-inference
[TNNLS 2022] Significance tests of feature relevance for a black-box learner
SkadiEye/deepTL
Deep Treatment Learning (R)
CN-TU/adversarial-recurrent-ids
Contact: Alexander Hartl, Maximilian Bachl, Fares Meghdouri. Explainability methods and Adversarial Robustness metrics for RNNs for Intrusion Detection Systems. Also contains code for "SparseIDS: Learning Packet Sampling with Reinforcement Learning" (branch "rl").
jpmorganchase/cf-shap
Counterfactual SHAP: a framework for counterfactual feature importance
sile/fanova
A Rust implementation of fANOVA (functional analysis of variance)
kingychiu/target-permutation-importances
A Python Package that computes Target Permutation Importances (Null Importances) of a machine learning model.
abdullah-al-masud/msdlib
This is a custom library for data processing, visualization and machine learning tools.
alifrmf/Customer-Segmentation-Using-Clustering-Algorithms
Customer Segmentation Using Unsupervised Machine Learning Algorithms
Broundal/Pytolemaic
Toolbox for analysis of model's quality and model's description. For further details see
sharmaroshan/Heart-UCI-Dataset
Analyzing the Features which leads to heart diseases and visualizing the models' performance and important features using eli5, shap and pdp.
haghish/shapley
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
mansipatel2508/Network-Intrusion-Detection-with-Feature-Extraction-ML
The given information of network connection, model predicts if connection has some intrusion or not. Binary classification for good and bad type of the connection further converting to multi-class classification and most prominent is feature importance analysis.
JonathanCrabbe/CARs
This repository contains the implementation of Concept Activation Regions, a new framework to explain deep neural networks with human concepts. For more details, please read our NeurIPS 2022 paper: 'Concept Activation Regions: a Generalized Framework for Concept-Based Explanations.