Perform inference on algorithm-agnostic variable importance in Python
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May 12, 2022 - Python
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variable-importance
Here are 7 public repositories matching this topic...
Automated Bidirectional Stepwise Selection On Python
python science data machine-learning backward variable-importance variable-selection feature-selection bidirectional features feature forward stepwise-regression
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Jul 4, 2019 - Python
Reproduce analyses from "Efficient nonparametric statistical inference on population feature importance using Shapley values"
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Aug 3, 2020 - Python
Simulating Supervised Learning Data
machine-learning simulation variable-importance regression feature-selection supervised-learning classification signal-to-noise
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Mar 12, 2019 - Python
Code for Measuring Variable Importance in Heterogeneous Treatment Effects
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Jul 21, 2025 - Python
Dissertation project exploring cyberattack detection. Implements Artificial Neural Networks on the HIKARI-2022 dataset, with extensive hyperparameter tuning, evaluation metrics, and variable importance analysis using DeepSHAP and PFI.
neural-network variable-importance anomaly-detection cyberattack permutation-feature-importance deepshap
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May 7, 2025 - Python
In this repository you find a python program and the prints and 3D-visualization of it. After the KNN-Classification I wanted to know which variables have the most relevance for the results. One approach for this is the Principal-Component-Analysis (PCA). More details in the python program as comments.
sqlalchemy numpy variable-importance pandas matplotlib standardization principal-component-analysis 3d-printing sklearn-library labelencoder
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Feb 1, 2025 - Python
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