Extracts hidden correlations between high-dimensional datasets
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Updated
May 13, 2020 - Jupyter Notebook
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relevant-feature-analysis
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High data dimensionality and irrelevant features can negatively impact the performance of machine learning algorithms. This repository implements the Permutation feature importance method to enhance the performance of some machine learning models by identifying the contribution of each feature used.
feature-selection classification optimization-methods decision-trees predictive-modeling support-vector-machines optimization-algorithms feature-importance k-nearest-neighbors permutation-importance relevant-feature-analysis wrapper-methods feature-selection-methods permutation-feature-importance
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Mar 12, 2024 - Jupyter Notebook
Post-hoc analysis of relevant spatiotemporal features for speech decoding using a linear support vector classifier and LDA. The post-hoc analysis is made by using the SHAP technique
linear-models fnirs interpretable-machine-learning shap shapley-value relevant-feature-analysis neural-decoding shapley-additive-explanations speech-decoding perceived-speech
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Jun 10, 2025 - Python
Opportunities and challenges in partitioning the graph measure space of real-world networks
machine-learning social-networks feature-selection networkx feature-extraction networks complex-networks network-measures network-analysis igraph affiliation-networks ppi-networks brain-networks skewed-data relevant-feature-analysis network-classification unbalanced-datasets transportation-networks web-networks metabolic-networks
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Feb 1, 2021 - Jupyter Notebook
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