#Feature-selection
Showing 58 of 58 repositories tagged #feature-selection, ranked by stars
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.
Features selector based on the self selected-algorithm, loss function and validation method
mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.
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).
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
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
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
Code repository for the online course Feature Selection for Machine Learning
Data Science Feature Engineering and Selection Tutorials
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
Everything is Linkable
A power-full Shapley feature selection method.
Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.
Python3 binding to mRMR Feature Selection algorithm (currently not maintained)
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
This repository contains an in-depth analysis of the Intrusion Detection Evaluation Dataset (CIC-IDS2017) for Intrusion Detection, showcasing the implementation and comparison of different machine learning models for binary and multi-class classification tasks.
Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector
Feature Selection for Clustering
Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python
Python framework for interpretable protein prediction
Simple Transparent End-To-End Automated Machine Learning Pipeline for Supervised Learning in Tabular Binary Classification Data
Analyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.
A python library to automate feature selection process for machine learning projects.
A complete A-Z guide to Machine Learning and Data Science using Python. Includes implementation of ML algorithms, statistical methods, and feature selection techniques in Jupyter Notebooks. Follow Coursesteach for tutorials and updates.
This repository contains the code and datasets for creating the machine learning models in the research paper titled "Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach"
New Polars implementation of the classic featurewiz MRMR algorithm. Created by Ram Seshadri. Collaborators welcome.
This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.
Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.
Python code source for features selection 👨🔬 series on medium website. 📰
Code repository for the book Feature Selection in Machine Learning
Deep learning methods for feature selection in gene expression autism data.
OmicSelector - Environment, docker-based application and R package for biomarker signiture selection (feature selection) & deep learning diagnostic tool development from high-throughput high-throughput omics experiments and other multidimensional datasets. Initially developed for miRNA-seq, RNA-seq and qPCR.
Hyperspectral CNN compression and band selection
This repository includes code for the paper "Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection" accepted in AutonomousCyber, ACM CCS, 2024.
Script to extract CNN deep features with different ConvNets, and then use them for an Image Classification task with a SVM classifier with lineal kernel over the following small datasets: Soccer [1], Birds [2], 17flowers [3], ImageNet-6Weapons[4] and ImageNet-7Arthropods[4].
Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset
A fast canonical-correlation-based search algorithm for feature selection, system identification, data pruning, etc.
This repository contains projects I have worked on for Data Cleaning and Manipulation in Python.
Adaptive Reinforcement Learning of curious AI basketball agents
This project is an educational, pure JavaScript library designed to help developers and students understand the inner workings of ML algorithms without the magic of external libraries.
📊 A practical toolkit for feature engineering and selection in Python. Explore EDA, handle missing data and outliers, scale/encode/transform features, discretize, and select via filter, wrapper, embedded, shuffling, and hybrid methods. Comes with datasets, clean notebooks, reusable utils, and clear visuals for fast, reproducible ML. Workflows.
Explore a collection of Jupyter notebooks that guide you through various stages of the machine learning pipeline. From data analysis and feature engineering to model training and deployment, these notebooks provide practical insights for both beginners and experienced data enthusiasts. Let's dive into the world of data-driven decision-making! 📊🚀"