Projects sharing features with Sklearn Evaluation

SigOpt wrappers for scikit-learn methods

scikit-learn inspired API for CRFsuite

PySpark Scikit-learn = Sparkit-learn

mlpack is a header-only C++ machine learning library that defines matrix types as compile-time templates, enabling flexible numeric precision and memory layout without runtime overhead. Its core identity is built around a template metaprogramming architecture that allows algorithms to be included selectively as independent modules, reducing binary size, and supports compile-time serialization of neural network parameters by deducing matrix types and structure at compile time. The library distinguishes itself through a multi-language binding framework that automatically generates bindings for

Simple structured learning framework for python

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

Use evolutionary algorithms instead of gridsearch in scikit-learn

A scikit-learn based module for multi-label et. al. classification

scikit-opt is a Python optimization library and numerical framework designed to solve complex global optimization problems. It provides a suite of metaheuristic algorithms and tools for finding global minima or maxima of objective functions. The library implements a variety of nature-inspired and swarm intelligence algorithms, including Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Simulated Annealing, and Ant Colony Optimization. It includes specialized solvers for discrete combinatorial challenges, such as the Traveling Salesman Problem. The framework supports th

CausalML is a machine learning library for causal inference, providing tools to estimate treatment effects and causal impacts using experimental and observational data. It functions as a framework for uplift modeling and the estimation of heterogeneous treatment effects to distinguish causation from correlation. The library focuses on identifying how different user segments respond to specific interventions. This includes calculating the incremental gain of target metrics to optimize marketing campaigns, targeting high-response customer segments, and personalizing user engagement through the

open-source feature selection repository in python

Sequence learning toolkit for Python

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.

Multiple Pairwise Comparisons (Post Hoc) Tests in Python

dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and utilities for building predictive modeling applications and performing statistical analysis on large datasets within native C++ environments. The project functions as a binding library that wraps low-level C++ machine learning algorithms into high-level Python scripting interfaces. This allows for the integration of high-performance native implementations with Python for machine learning development. The framework covers the implementation of predictive models, the execution of mach

Python package for Bayesian Machine Learning with scikit-learn API

Python implementation of the rulefit algorithm

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

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

Deepchecks is a machine learning model validation framework and MLOps testing library. It serves as an AI data quality suite and performance evaluator designed to verify the integrity and performance of models and datasets from research through production. The project functions as a model monitoring tool for tracking data drift and performance degradation in production environments. It allows for the creation of custom validation suites and utilizes a pluggable check architecture to automate quality checks within continuous integration pipelines. The framework covers a broad range of capabil

CONTRIBUTORS WELCOME Generalized Additive Models 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

GoLearn is a machine learning library for the Go programming language. It provides a supervised learning framework and a toolkit for building, training, and evaluating predictive models through a standardized interface. The project implements a data frame system that loads CSV files into structured grids for matrix operations. It includes a preprocessing library for discretizing continuous variables and a model evaluation toolkit that utilizes confusion matrices and cross-validation to measure precision and recall. The library covers data engineering and management, including the ability to

A C library for product recommendations/suggestions using collaborative filtering (CF)

TPOT is a Python automated machine learning tool and pipeline framework. It automatically searches, selects, and tunes machine learning algorithms and hyperparameters to identify the most effective model architecture. The system utilizes genetic programming to optimize these pipelines through evolutionary algorithms. To accelerate the search process, it functions as a multi-core evaluator that runs parallel training workflows across multiple processor cores. The framework supports the definition of custom objective functions to optimize pipelines based on specific performance metrics.

Apache Mahout - an environment for quickly creating scalable, performant machine learning applications.