Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python

Python code source for features selection 👨‍🔬 series on medium website. 📰

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.

Python convenience/wrapper methods and classes for setting up FIFO message queues and/or pub-sub message brokers for clients to use for exchanging messages through Redis.

Automated testing project for QTrip, a travel website, using Java, Selenium, and TestNG. Implemented Page Object Model for 4+ test cases, integrated Apache POI for dynamic data, and utilized Extent Reports for detailed test reporting.

Answer to how to select variables in data set and build simpler, faster, more reliable and interpretable ML models

Feature Selection Using Python (Forward Selection and Backward Elimination)

in this section, we will be wrapper feature selection on the diabetes dataset

Automated Machine Learning Feature Engineering library with advanced feature selection methods (Filter, mRMR, Wrapper, Embedded)

In this project, I will perform feature selection using three different wrapper methods on a dataset obtained from a survey about eating habits and weight. The goal is to identify a smaller subset of features that can accurately predict whether a survey respondent is obese or not.

Feature selection for real ML pipelines: filters, wrappers, and metaheuristics with cross-validation, reproducibility, and bundled benchmarks.

Music genre classification done in two different ways: 1.Traditional ML and 2.Temporal feature integration/Fusion of decisions

Trabajo fin de máster de ciencia de datos UC-UIMP-CSIC 2018-2019

A project aimed at predicting variables of interest within the dataset.

Comparative analysis of 6 feature selection methods for high-dimensional neural decoding (p >> n) with 11,190 features and 683 samples

Implementation of wrapper methods for selecting optimal feature subsets to improve model performance.