Multivariate Adaptive Regression Spline Models: Build regression models using the techniques in Friedman's papers "Fast MARS" and "Multivariate Adaptive Regression Splines". (The term "MARS" is [...]

Model Monitor is a Java toolkit for the systematic evaluation of classifiers under changes in distribution. It provides methods for detecting distribution shifts in data, comparing the performance [...]

Model-Based Boosting: Functional gradient descent algorithms (boosting) for optimizing general loss functions utilizing componentwise least squares, either of parametric linear form or smoothing [...]

KeplerWeka represents the integration of all the functionality of the WEKA Machine Learning Workbench [1] into the open-source scientific workflow Kepler [2]. Among them are classification, [...]

Debellor is an open source extensible data mining platform which provides common architecture for data processing algorithms of various types.

BioSig is a software library for biomedical signal processings. Besides several other modules, one modul (t400) provides a common interface (train_sc.m and test_sc.m) to various classification [...]

A Laboratory for Recursive Partytioning: A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed [...]

Bayesian treed Gaussian process models: Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes with jumps to the limiting linear model (LLM). Special [...]

JNCC2 is the open-source implementation of the Naive Credal Classifier2 (NCC2), i.e., an extension of Naive Bayes towards imprecise probabilities, designed to deliver robust classifications even on [...]

CVX is a Matlab-based modeling system for convex optimization. CVX turns Matlab into a modeling language, allowing constraints and objectives to be specified using standard Matlab expression syntax. [...]

Recursive Partitioning: Recursive partitioning and regression trees

Modular toolkit for Data Processing (MDP) is a library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software.

Classification and Regression Training: Misc functions for training and plotting classification and regression models

Breiman and Cutler's random forests for classification and regression: Classification and regression based on a forest of trees using random inputs.

PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easy-to-use yet still powerful algorithms for machine learning tasks, including a variety of predefined [...]

Eblearn is an object-oriented C++ library that implements various

Experiment Databases for Machine Learning is a large public database of machine learning experiments as well as a framework for producing similar databases for specific goals. It provides a way to [...]

JProGraM is an open-source Java library which can be used for learning the following probabilistic models from data: Bayesian networks, Markov random fields, hybrid random fields, probabilistic [...]

The Chestnut Machine Learning Library is a suite of machine learning algorithms written in Python with some code written in C for efficiency. Most algorithms are called with a simple, functional API [...]

This library provides Python functions for agglomerative clustering. Its features include