Machine Learning in R

For extensive instructor led learning

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

Features selector based on the self selected-algorithm, loss function and validation method

A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.

This repository contains the code related to Natural Language Processing using python scripting language. All the codes are related to my book entitled "Python Natural Language Processing"

Leave One Feature Out Importance

This project demonstrates how to apply machine learning algorithms to distinguish "good" stocks from the "bad" stocks.

Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

A fast xgboost feature selection algorithm

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

scikit-learn compatible implementation of stability selection.

Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

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.

Stock for Deep Learning and Machine Learning

Feature Selection using Genetic Algorithm (DEAP Framework)

Methods with examples for Feature Selection during Pre-processing in Machine Learning.

A Machine Learning Approach of Emotional Model

Machine Learning with the NSL-KDD dataset for Network Intrusion Detection

Versatile Nonlinear Feature Selection Algorithm for High-dimensional Data

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

Search the best feature subset for you classification mode

Python3 binding to mRMR Feature Selection algorithm (currently not maintained)

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

A FEAture Selection Toolbox for C/C+, Java, and Matlab/Octave.

Ablator is a Service that enables you to roll out functionalities at your own pace, and perform good A/B testing.

Efficient Algorithms for L0 Regularized Learning