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.

Extracts hidden correlations between high-dimensional datasets

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

Opportunities and challenges in partitioning the graph measure space of real-world networks