feature-selection

For extensive instructor led learning

Machine Learning in R

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

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

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

Deep Learning and Machine Learning stocks represent a promising long-term or short-term opportunity for investors and traders.

Leave One Feature Out Importance

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

EvalML is an AutoML library written in python.

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

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

Use advanced feature engineering strategies and select best features from your data set with a single line of code.

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

Easy to use Python library of customized functions for cleaning and analyzing data.

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

Fast Best-Subset Selection Library

Feature Selection using Genetic Algorithm (DEAP Framework)

Data Science Feature Engineering and Selection Tutorials