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

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