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.
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, nonnegative matrix factorization, sequential feature selection, and more
Design of experiments (DOE); survival and reliability analysis; statistical process control
Parallel or distributed computation of statistical functions