Model interpretability and understanding for PyTorch
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Aug 30, 2026 - Python
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feature-importance
Here are 441 public repositories matching this topic...
XAI - An eXplainability toolbox for machine learning
machine-learning ai evaluation ml artificial-intelligence upsampling bias interpretability feature-importance explainable-ai explainable-ml xai imbalance downsampling explainability bias-evaluation machine-learning-explainability xai-library
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Nov 29, 2025 - Python
Leave One Feature Out Importance
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Feb 14, 2025 - Python
Shapley Interactions and Shapley Values for Machine Learning
python machine-learning game-theory interpretability feature-importance explainable-ai banzhaf-index shapley interpretable-machine-learning shap explainability feature-attribution feature-interactions shapley-interactions shapiq
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Sep 1, 2026 - Python
Features selector based on the self selected-algorithm, loss function and validation method
data-science machine-learning feature-selection feature-extraction feature-engineering greedy-search feature-importance
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May 8, 2019 - Python
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.
statistics deep-neural-networks neural-network random-forest pandas-dataframe machine-learning-algorithms soccer data-visualization seaborn football-data predictions soccer-matches imbalanced-learning feature-importance soccer-betting optuna feature-analysis imbalanced-learn interpretability-methods upcoming-games
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Apr 16, 2026 - Python
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.
feature-selection logistic-regression feature-engineering regression-models predictor multiple-regression feature-importance classification-model relative-importance dominance-analysis r-square shapley-value keydrivers dominance dominance-statistics predictor-importance pseudo-r-square
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Aug 17, 2023 - Python
In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbofan engine.
regression classification cnn-keras lstm-neural-networks feature-importance predictive-maintenance rul-prediction exponential-degradation similarity-based-model
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Aug 3, 2022 - Jupyter Notebook
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
python data-science machine-learning ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-network fairness interpretability cdep feature-importance recurrent-neural-network interpretable-deep-learning explainable-ai explainability fairness-ml
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Mar 22, 2021 - Jupyter Notebook
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
python data-science machine-learning statistics deep-neural-networks ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-networks acd interpretation iclr interpretability feature-importance explainable-ai explainability
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Aug 25, 2021 - Jupyter Notebook
Beta Machine Learning Toolkit
data-science machine-learning ai deep-learning random-forest clustering julia ml regression artificial-intelligence neural-networks pca classification autoencoder imputation decision-trees feature-importance
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Jul 24, 2026 - Julia
A Julia package for interpretable machine learning with stochastic Shapley values
julia feature-importance shapley interpretable-machine-learning iml shap shapley-value stochastic-shapley-values
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May 6, 2024 - Julia
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
package machine-learning r ensemble r-package causality causal-inference feature-importance causal-networks shapley interpretable-machine-learning iml shap shapley-value shapley-values
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Jun 9, 2020 - R
Adding feature_importances_ property to sklearn.cluster.KMeans class
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Sep 2, 2023 - Jupyter Notebook
Awesome papers on Feature Selection
awesome correlation variable-selection feature-selection dimensionality-reduction awesome-list mutual-information feature-importance
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Feb 14, 2026
Official repository of the paper "Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance", M. Carletti, M. Terzi, G. A. Susto.
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Aug 7, 2024 - Jupyter Notebook
Variance-based Feature Importance in Neural Networks
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Jun 28, 2025 - Python
Routines and data structures for using isarn-sketches idiomatically in Apache Spark
python scala spark apache-spark aggregator variable-importance pyspark dataset t-digest data-sketching datasets dataframe dataframes data-sketches sketching-algorithm spark-ml feature-importance udaf
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May 27, 2024 - Scala
Customer churn prediction with Python using synthetic datasets. Includes data generation, feature engineering, and training with Logistic Regression, Random Forest, and Gradient Boosting. Improved pipeline applies hyperparameter tuning and threshold optimization to boost recall. Outputs metrics, reports, and charts.
python data-science machine-learning random-forest classification logistic-regression predictive-modeling gradient-boosting portfolio-project feature-importance roc-auc customer-analytics precision-recall customer-churn
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Jul 7, 2026 - Python
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
python machine-learning statistics ai random-forest scikit-learn ml artificial-intelligence stats feature-engineering ensemble-model boosting interpretability random-forests feature-importance explainable-ai
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Feb 11, 2021 - Python
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