feature-selection - 技术专题

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

Feature engineering and selection open-source Python library compatible with sklearn.

For extensive instructor led learning

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

Machine Learning in R

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.

Leave One Feature Out Importance

EvalML is an AutoML library written in python.

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.

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.

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

mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.

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

Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

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

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

Fast Best-Subset Selection Library

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

Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).