There are 43 repositories under feature-selection topic.
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
Feature engineering package with sklearn like functionality
For extensive instructor led learning
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
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
Features selector based on the self selected-algorithm, loss function and validation method
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.
A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.
Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
Fast Best-Subset Selection Library
A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
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
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.
Methods with examples for Feature Selection during Pre-processing in Machine Learning.
Feature Selection using Genetic Algorithm (DEAP Framework)
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"
Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs
Awesome Domain Adaptation Python Toolbox
ML hyperparameters tuning and features selection, using evolutionary algorithms.
Code repository for the online course Feature Selection for Machine Learning
Data Science Feature Engineering and Selection Tutorials
This toolbox offers 13 wrapper feature selection methods (PSO, GA, GWO, HHO, BA, WOA, and etc.) with examples. It is simple and easy to implement.
zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.
Machine Learning with the NSL-KDD dataset for Network Intrusion Detection
A Machine Learning Approach of Emotional Model
Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
A fast xgboost feature selection algorithm
scikit-learn compatible implementation of stability selection.
A power-full Shapley feature selection method.