Statistics and Machine Learning Toolbox™ provides functions and apps to describe, analyze, and model data. You can use descriptive statistics and plots for exploratory data analysis, fit probability distributions to data, generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models.
For multidimensional data analysis, Statistics and Machine Learning Toolbox provides feature selection, stepwise regression, principal component analysis (PCA), regularization, and other dimensionality reduction methods that let you identify variables or features that impact your model.
The toolbox provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models. Many of the statistics and machine learning algorithms can be used for computations on data sets that are too big to be stored in memory.
Run statistical computations in parallel to gain speed and to reduce the execution time of your program or functions.Learn more
Discover more about Statistics and Machine Learning Toolbox by exploring these resources.
Explore documentation for Statistics and Machine Learning Toolbox functions and features, including release notes and examples.
Browse the list of available Statistics and Machine Learning Toolbox functions.
View system requirements for the latest release of Statistics and Machine Learning Toolbox.
View articles that demonstrate technical advantages of using Statistics and Machine Learning Toolbox.
Read how Statistics and Machine Learning Toolbox is accelerating research and development in your industry.
Find answers to questions and explore troubleshooting resources.
Statistics and Machine Learning Toolbox apps enable you to quickly access common tasks through an interactive interface.
Statistics and Machine Learning Toolbox requires: MATLAB
Use Statistics and Machine Learning Toolbox to solve scientific and engineering challenges: