This package contains a generic implementation of greedy Information Theoretic Feature Selection (FS) methods. The implementation is based on the common theoretic framework presented by Gavin Brown. Implementations of mRMR, InfoGain, JMI and other commonly used FS filters are provided.

All Relevant Feature Selection

An improved implementation of the classical feature selection method: minimum Redundancy and Maximum Relevance (mRMR).

This is a complete guide on how to do Pyradiomics based feature extraction and then, build a model to calculate the grade of glioma.

Implementations of various feature selection methods

This is an App developed in Python to implement the algorithm for minimum redundancy maximum ralevance. The formulation was based on a research paper from Chris Ding and Hanchuan Peng (Minimum Redundancy Feature Selection from Microarray Gene Expression Data).

Implementation of various feature selection methods using TensorFlow library.

Maximum Relevance Minimum Redundancy for big datasets

Conformal Inference tools using python

Feature selection in Apache Spark using Minimum Redundancy and Maximum Relevance

scikit-learn compatible MRMR feature selection

Diabetes Prediction using Three Machine Learning Algorithms - Logistic Regression, Random Forest & SVM

Master MVA - Time Series Project

A project that focuses on implementing a hybrid approach that modifies the identification of biomarker genes for better categorization of cancer. The methodology is a fusion of MRMR filter method for feature selection, steady state genetic algorithm and a MLP classifier.

Feature engineering, selection and XGBoost modeling for the Kaggle House Prices Regression competition.

Some Hybrid Machine Learning Algorithms 🤖 that I developed during my 4th Semester 📓

Cardiovasular Disease Detection using Naive Bayes, Logistic Regression, Random Forest & Support Vector Machine, while comparing the Naive Bayes models with the rest. LIME was also used to explain the predictions of the model.