Open-source alternatives to Sklearn Crfsuite

Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

open-source feature selection repository in python

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

Use evolutionary algorithms instead of gridsearch in scikit-learn

SigOpt wrappers for scikit-learn methods

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

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

Multiple Pairwise Comparisons (Post Hoc) Tests in Python

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

Sequence learning toolkit for Python

mlxtend is a pure Python machine learning extension library that provides additional tools for association rule mining, ensemble learning, and feature selection. It is built on numpy and pandas, with all data operations accepting and returning pandas DataFrames, and custom estimators inherit from scikit-learn’s base classes to offer a uniform fit-predict interface compatible with grid search. The library implements the Apriori algorithm for mining frequent itemsets from transaction data and generating association rules with confidence and lift metrics. For classification, it combines multiple

Python implementation of the rulefit algorithm

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

A modular active learning framework for Python

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

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

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

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.

Fastai is a high-level deep learning library built on PyTorch that provides a unified interface for managing the entire machine learning lifecycle. It functions as a comprehensive training toolkit, abstracting hardware management and automating complex training loops to simplify the construction and execution of neural network models. The framework is distinguished by its notebook-centric development environment and a type-dispatching data pipeline that automatically applies transformations based on input data formats. It emphasizes transfer learning through discriminative layer-wise optimiza

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

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.