Statistics and Machine Learning Toolbox™ provides functions and apps to describe, analyze, and model data using statistics and machine learning. 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 analyzing multidimensional data, Statistics and Machine Learning Toolbox lets you identify key variables or features that impact your model with sequential feature selection, stepwise regression, principal component analysis, regularization, and other dimensionality reduction methods. 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.
Regression techniques, including linear, generalized linear, nonlinear, robust, regularized, ANOVA, and mixed-effects models
Repeated measures modeling for data with multiple measurements per subject
Univariate and multivariate probability distributions, including copulas and Gaussian mixtures
Random and quasi-random number generators and Markov chain samplers
Hypothesis tests for distributions, dispersion, and location, and design of experiments (DOE) techniques for optimal, factorial, and response surface designs
Classification Learner app and algorithms for supervised machine learning, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, and discriminant analysis
Unsupervised machine learning algorithms, including k-means, k-medoids, hierarchical clustering, Gaussian mixtures, and hidden Markov models