Repository Index

Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.

• data-science
• python
• machine-learning
• pycaret

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

• machine-learning
• decision-trees
• gradient-boosting
• gbm

Statistical Machine Intelligence & Learning Engine

• machine-learning
• regression
• clustering
• manifold-learning

mlpack: a fast, header-only C++ machine learning library

• machine-learning-library
• c-plus-plus
• deep-learning
• nearest-neighbor-search

Scalable and user friendly neural :brain: forecasting algorithms.

• deep-learning
• forecasting
• esrnn
• nbeats

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

• machine-learning
• python
• statistics
• scikit-learn

Genetic Programming in Python, with a scikit-learn inspired API

• genetic-programming
• machine-learning
• python
• scikit-learn

Machine Learning in R

• machine-learning
• data-science
• tuning
• cran

ThunderSVM: A Fast SVM Library on GPUs and CPUs

• classification
• regression
• one-class-learning
• cuda

MLBox is a powerful Automated Machine Learning python library.

• machine-learning
• auto-ml
• kaggle
• deep-learning

mlr3: Machine Learning in R - next generation

• machine-learning
• data-science
• classification
• regression

Artificial Neural Network

• neural-network
• deep-learning
• classification
• regression

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

• python
• sklearn
• data-science
• framework

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

• svm
• regression
• classification
• machine-learning

Visually Validate Playwright Tests Without Flaky Selectors

• ai
• api
• cypress
• cypress-io

DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.

Machine Learning Module for Single Layer Perceptron ML models, written in Rust for Typescript.

• classification
• deno
• machine-learning
• regression