Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox™ provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering 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 either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.
For multidimensional data analysis and feature extraction, the toolbox provides principal component analysis (PCA), regularization, dimensionality reduction, and feature selection methods that let you identify variables with the best predictive power.
The toolbox provides supervised, semi-supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted decision trees, k-means, and other clustering methods. You can apply interpretability techniques such as partial dependence plots and LIME, and automatically generate C/C++ code for embedded deployment. Many toolbox algorithms can be used 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 and semi-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/C++ code and MEX functions for Statistics and Machine Learning Toolbox functions
Apply statistics and machine learning methods to industry-specific workflows