mrmr

Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.

All Relevant Feature Selection

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.

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.

scikit-learn compatible MRMR feature selection

Maximum Relevance Minimum Redundancy for big datasets

Conformal Inference tools using python

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

Autonomous feature engineering engine utilizing zero-copy Apache Arrow for high-performance dimensionality expansion and non-linear signal isolation.

Feature selection in Apache Spark using Minimum Redundancy and Maximum Relevance

Machine-learning framework for tabular data: one entry point to train, evaluate, calibrate, and ensemble across scikit-learn, CatBoost, LightGBM, XGBoost, and PyTorch Lightning models.

A hybrid causal feature selection framework using mRMR, local skeleton constraints, and continuous neural graph optimization for high-dimensional RNA-Seq biomarker discovery.

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

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.