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open-source feature selection repository in python
scikit-image is a Python image processing library and scientific image analysis toolkit. It provides a framework for digital image processing and computer vision, utilizing numerical arrays for pixel-level manipulations. The library enables the quantification of image properties and the detection of visual features, such as edges and blobs. It includes tools for image segmentation and the extraction of textures and patterns to characterize objects within visual data. Capabilities cover image manipulation through color space conversion, geometric transformations, and digital restoration. It a
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
The main features of epistasislab/scikit-rebate are: Feature Selection, General Machine Learning, Machine Learning Frameworks, Machine Learning Packages, Feature Engineering.
Open-source alternatives to epistasislab/scikit-rebate include: jundongl/scikit-feature — open-source feature selection repository in python. scikit-image/scikit-image — scikit-image is a Python image processing library and scientific image analysis toolkit. It provides a framework for… danielhanchen/hyperlearn — 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old. amazaspshumik/sklearn-bayes — Python package for Bayesian Machine Learning with scikit-learn API. csinva/imodels — Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). aksnzhy/xlearn — High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization…
