feature-importance

There are 9 repositories under feature-importance topic.

pytorch / captum
Model interpretability and understanding for PyTorch
interpretability interpretable-ai interpretable-ml feature-importance feature-attribution
Language:Python 5403
EthicalML / xai
XAI - An eXplainability toolbox for machine learning
explainability xai ml ai bias artificial-intelligence bias-evaluation explainable-ai explainable-ml machine-learning machine-learning-explainability interpretability xai-library evaluation imbalance upsampling downsampling feature-importance
Language:Python 1200
aerdem4 / lofo-importance
Leave One Feature Out Importance
feature-importance machine-learning data-science feature-selection explainable-ai
Language:Python 838
duxuhao / Feature-Selection
Features selector based on the self selected-algorithm, loss function and validation method
machine-learning feature-engineering feature-selection data-science greedy-search feature-importance feature-extraction
Language:Python 681
shapiq
mmschlk / shapiq
Shapley Interactions and Shapley Values for Machine Learning
python feature-interactions explainability interpretability machine-learning shap shapley shapiq shapley-interactions banzhaf-index explainable-ai feature-importance game-theory interpretable-machine-learning feature-attribution
Language:Python 612
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.
soccer statistics feature-analysis deep-neural-networks feature-importance imbalanced-learning interpretability-methods machine-learning-algorithms neural-network random-forest soccer-betting soccer-matches football-data predictions upcoming-games imbalanced-learn data-visualization pandas-dataframe seaborn optuna
Language:Python 431
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.
classification-model dominance dominance-analysis dominance-statistics feature-engineering feature-importance feature-selection keydrivers logistic-regression multiple-regression predictor predictor-importance pseudo-r-square r-square regression-models relative-importance shapley-value
Language:Python 160
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.
rul-prediction predictive-maintenance regression classification lstm-neural-networks exponential-degradation similarity-based-model cnn-keras feature-importance
Language:Jupyter Notebook 149
hierarchical-dnn-interpretations
csinva / hierarchical-dnn-interpretations
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
interpretability neural-network machine-learning convolutional-neural-networks pytorch interpretation explainability deep-learning explainable-ai artificial-intelligence ml ai python data-science jupyter-notebook deep-neural-networks acd statistics feature-importance iclr
Language:Jupyter Notebook 128
deep-explanation-penalization
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
interpretability neural-network machine-learning convolutional-neural-network pytorch explainability deep-learning explainable-ai artificial-intelligence ml ai python data-science jupyter-notebook feature-importance recurrent-neural-network fairness fairness-ml cdep interpretable-deep-learning
Language:Jupyter Notebook 128
BetaML.jl
sylvaticus / BetaML.jl
Beta Machine Learning Toolkit
ai artificial-intelligence autoencoder classification clustering data-science decision-trees deep-learning feature-importance imputation julia machine-learning ml neural-networks pca random-forest regression
Language:Julia 98
ShapML.jl
nredell / ShapML.jl
A Julia package for interpretable machine learning with stochastic Shapley values
feature-importance iml interpretable-machine-learning julia shap shapley shapley-value stochastic-shapley-values
Language:Julia 90
shapFlex
nredell / shapFlex
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
shapley shapley-value ensemble machine-learning package r feature-importance shapley-values iml interpretable-machine-learning r-package shap causality causal-inference causal-networks
Language:R 74
YousefGh / kmeans-feature-importance
Adding feature_importances_ property to sklearn.cluster.KMeans class
feature-importance interpretability k-means keyword-extraction
Language:Jupyter Notebook 64
mlpapers / feature-selection
Awesome papers on Feature Selection
feature-selection feature-importance variable-selection mutual-information correlation dimensionality-reduction awesome awesome-list
35
isarn / isarn-sketches-spark
Routines and data structures for using isarn-sketches idiomatically in Apache Spark
apache-spark data-sketches data-sketching scala t-digest udaf aggregator spark dataset datasets dataframe dataframes sketching-algorithm python feature-importance pyspark spark-ml variable-importance
Language:Scala 29
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.
anomaly-detection feature-importance isolation-forest
Language:Jupyter Notebook 28
disentangled-attribution-curves
csinva / disentangled-attribution-curves
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
interpretability random-forests machine-learning scikit-learn python statistics explainable-ai boosting ml ai stats random-forest ensemble-model feature-importance feature-engineering artificial-intelligence
Language:Python 27
rebelosa / feature-importance-neural-networks
Variance-based Feature Importance in Neural Networks
neural-networks feature-importance deep-learning
Language:Jupyter Notebook 27
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.
deep-learning feature-selection feature-importance
Language:Jupyter Notebook 26
CederGroupHub / s4
Solid-state synthesis science analyzer. Thermo, features, ML, and more.
thermodynamics solid-state-synthesis machine-learning feature-importance materials-science materials-informatics
Language:Jupyter Notebook 24
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'.
machine-learning artificial-intelligence explainable-ai latent-space latent-representations interpretability method example-based-explanations feature-importance neurips-2021 neurips neural-networks
Language:Python 24
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.
hdb-resale-prices linear-regression random-forest machine-learning feature-engineering feature-importance data-visualization
Language:Python 23
AliAmini93 / Telecom-Churn-Analysis
Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.
business-intelligence churn-prediction datascience feature-engineering feature-importance machine-learning model-optimization shap xgboost
Language:Jupyter Notebook 22
jpmorganchase / cf-shap
Counterfactual SHAP: a framework for counterfactual feature importance
explainability explainable-ai explainable-artificial-intelligence explainable-ml explanations feature-attribution feature-importance machine-learning shap shapley shapley-values tree-based xgboost algorithmic-recourse counterfactual-explanations counterfactuals facct2022 explainable-machine-learning
Language:HTML 20
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").
adversarial-machine-learning rnn rnn-lstm explainability feature-importance feature-sensitivity adversarial-robustness
Language:TeX 18
RiccardoSpolaor / Verbal-Explanations-of-Spatio-Temporal-Graph-Neural-Networks-for-Traffic-Forecasting
An eXplainable AI system to elucidate short-term speed forecasts in traffic networks obtained by Spatio-Temporal Graph Neural Networks.
congestion-analysis explainable-ai feature-importance graph-neural-networks mcts-algorithm metr-la monte-carlo-tree-search natural-language-generation natural-language-processing nlp outcomes-analytics pems-bay post-hoc-explanation spatio-temporal-graph speed-prediction traffic-forecasting transparency trustworthy-ai xai stgnn
Language:Jupyter Notebook 18
statmlben / dnn-inference
Significance tests of feature relevance for a black-box learner
black-box-testing hypothesis-testing significance-testing xai feature-importance feature-selection image-inference statistical-inference statistical-tests
Language:Python 18
haghish / shapley
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
machine-learning machine-learning-algorithms shap-analysis shap-values shapely class-imbalance class-imbalance-problem feature-extraction feature-importance feature-selection shap shapley-additive-explanations shapley-decomposition shapley-value shapley-values shapleyvalue weighted-shap weighted-shap-confidence-interval weighted-shapley weighted-shapley-ci
Language:R 16
SkadiEye / deepTL
Deep Treatment Learning (R)
treatment-effects feature-selection feature-importance personalized-medicine deep-learning
Language:R 16
sile / fanova
A Rust implementation of fANOVA (functional analysis of variance)
fanova feature-importance hyperparameter-importance
Language:Rust 15
kingychiu / target-permutation-importances
A Python Package that computes Target Permutation Importances (Null Importances) of a machine learning model.
feature-importance feature-selection kaggle permutation-importance permutation-importances
Language:Python 14
abdullah-al-masud / msdlib
This is a custom library for data processing, visualization and machine learning tools.
data-processing data-science machine-learning filters time-series-analysis time-series-visualization data-visualization train-validation-test sequence-data-split feature-evaluator feature-importance pytorch-model classification-model pytorch-classification pytorch-regression
Language:Python 13
alifrmf / Customer-Segmentation-Using-Clustering-Algorithms
Customer Segmentation Using Unsupervised Machine Learning Algorithms
agglomerative-clustering cleaning-data clustering customer-segmentation data data-science dbscan-clustering exploratory-data-analysis feature-importance gaussian-mixture-clustering hirarchical-clustering hyperparameter-selection kmeans-clustering machine-learning-algorithms minibatchkmeans-clustering outlier-analysis python unsupervised-learning feature-selection
Language:Jupyter Notebook 13
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.
machine-learning data-visualization data-analysis feature-importance model-evaluation
Language:Jupyter Notebook 12
jpmorganchase / cf-shap-facct22
Counterfactual Shapley Additive Explanation: Experiments
facct2022 shapley explanations algorithmic-recourse counterfactual-explanations counterfactuals explainable-ai explainable-artificial-intelligence explainable-machine-learning explainable-ml feature-attention feature-importance machine-learning shap shapley-values tree-based xgboost
Language:Jupyter Notebook 11

feature-importance

pytorch / captum

EthicalML / xai

aerdem4 / lofo-importance

duxuhao / Feature-Selection

mmschlk / shapiq

kochlisGit / ProphitBet-Soccer-Bets-Predictor

dominance-analysis / dominance-analysis

archd3sai / Predictive-Maintenance-of-Aircraft-Engine

csinva / hierarchical-dnn-interpretations

laura-rieger / deep-explanation-penalization

sylvaticus / BetaML.jl

nredell / ShapML.jl

nredell / shapFlex

YousefGh / kmeans-feature-importance

mlpapers / feature-selection

isarn / isarn-sketches-spark

mattiacarletti / DIFFI

csinva / disentangled-attribution-curves

rebelosa / feature-importance-neural-networks

unnir / CancelOut

CederGroupHub / s4

JonathanCrabbe / Simplex

teyang-lau / HDB_Resale_Prices

AliAmini93 / Telecom-Churn-Analysis

jpmorganchase / cf-shap

CN-TU / adversarial-recurrent-ids

RiccardoSpolaor / Verbal-Explanations-of-Spatio-Temporal-Graph-Neural-Networks-for-Traffic-Forecasting

statmlben / dnn-inference

haghish / shapley

SkadiEye / deepTL

sile / fanova

kingychiu / target-permutation-importances

abdullah-al-masud / msdlib

alifrmf / Customer-Segmentation-Using-Clustering-Algorithms

sharmaroshan / Heart-UCI-Dataset

jpmorganchase / cf-shap-facct22