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.
Learn the basics of Statistics and Machine Learning Toolbox
Data import and export, descriptive statistics, visualization
Data frequency models, random sample generation, parameter estimation
t-test, F-test, chi-square goodness-of-fit test, and more
Unsupervised learning techniques to find natural groupings and patterns in data
Analysis of variance and covariance, multivariate ANOVA, repeated measures ANOVA
Linear, generalized linear, nonlinear, and nonparametric techniques for supervised learning
Supervised learning algorithms for binary and multiclass problems
PCA, factor analysis, feature selection, feature extraction, and more
Design of experiments (DOE); survival and reliability analysis; statistical process control
Analyze out-of-memory data
Parallel or distributed computation of statistical functions
Generate C code and MEX functions for toolbox functions.