Statistics and Machine Learning Toolbox

Analyze and model data using statistics and machine learning

Statistics and Machine Learning Toolbox provides functions and apps for statistical analysis and machine learning in MATLAB.

For statistical analysis, you can use descriptive statistics to explore data, fit probability distributions, test hypotheses, perform analysis of variance (ANOVA), plan experiments, validate measurement systems, and monitor processes. You can use functions for programmatic analysis and interactive apps for guided workflows such as distribution fitting, design of experiments (DOE), and Gage R&R.

For machine learning, you can train regression, classification, clustering, and other models interactively or programmatically. The toolbox supports feature engineering, dimensionality reduction, model interpretation, Simulink integration, and C/C++ code generation for deployment. For an interactive experience, you can use the Classification Learner or Regression Learner apps to explore data, select features, choose validation schemes, tune hyperparameters, and evaluate multiple algorithms side-by-side.

Statistical Analysis

Multidimensional scatter plot shows relationships between variables.

Explore and Visualize Data

Explore data with statistical plots, interactive graphics, and descriptive statistics. Summarize central tendency, dispersion, shape, correlation, and covariance.

Documentation | Examples

DOE Explorer app showing a linear regression fit summary with estimated coefficients, standardized effects, and ANOVA results.

Design of Experiments (DOE)

Design systematic experiments and analyze response data in the DOE Explorer app. Generate common experiment designs, fit linear models, review ANOVA and residual diagnostics, and export results for further analysis in MATLAB.

Documentation | Examples

X-bar and R control charts with center lines and control limits.

Statistical Process Control

Monitor process stability and capability with control charts, capability analysis, and Pareto analysis. Assess measurement system variation with the Gage R&R app.

Documentation | Examples

Failure data is shown with censored lifetime values.

Reliability Analysis

Model failure times, hazard rates, and survival curves from censored lifetime data, then compute mean time between failures (MTBF) from those estimates.

Documentation | Examples

Probability distribution fit evaluated against sample data.

Probability Distributions

Fit continuous and discrete distributions, evaluate goodness-of-fit with statistical plots, and compute probability density and cumulative distribution functions.

Documentation | Examples

ANOVA comparisons plot showing group means with confidence intervals.

Hypothesis Tests and ANOVA

Compare groups and evaluate factor effects with t-tests, nonparametric tests, distribution tests, and ANOVA. Use one-way, multiway, and repeated measures ANOVA with post hoc comparisons to interpret statistical differences.

Documentation (Hypothesis Tests, ANOVA) | Examples (Hypothesis Tests, ANOVA)

Machine Learning

Regression Learner app comparing trained regression models.

Classification and Regression

Interactively train, validate, and compare classification and regression models without writing code. Explore data, select features, choose validation schemes, tune hyperparameters, and export trained models or MATLAB code.

Documentation (Classification Learner, Regression Learner) | Examples (Classification, Regression)

MATLAB code example for fitting Gaussian process regression models and comparing prediction intervals.

Machine Learning Workflows

Build end-to-end machine learning workflows in MATLAB, from data preparation and feature engineering to model training, evaluation, and deployment. Create reusable pipelines, automate workflows, and scale experiments.

Documentation | Examples

Diagram showing MATLAB machine learning workflows that generate code for embedded hardware or compile models for enterprise systems.

Code Generation and Deployment

Deploy machine learning models to production systems and embedded targets. Generate optimized C/C++ code from trained models or integrate trained models into Simulink workflows.

Documentation | Examples

Partial least-squares regression coefficient plot showing predictor importance across features.

Feature Engineering

Extract, transform, and select features to improve predictive performance. Apply dimensionality reduction techniques, such as PCA, and feature selection to identify the most informative inputs for modeling.

Documentation | Examples

Machine learning pipeline for classification workflow components.

Machine Learning Pipelines

Define workflows that combine preprocessing, feature selection, and model training into reusable pipelines. Standardize experiments, reduce manual steps, and deploy consistent workflows across teams and applications.

Documentation | Examples

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Statistics and Machine Learning Toolbox FAQs

Statistics and Machine Learning Toolbox provides functions and apps to describe, analyze, and model data using descriptive statistics, visualizations, clustering, probability distributions, hypothesis tests, and supervised, semi-supervised, and unsupervised machine learning algorithms.

You can train models interactively using the Classification Learner and Regression Learner apps, or programmatically using AutoML, with algorithms including linear regression, support vector machines, boosted decision trees, and shallow neural networks.

The toolbox provides linear regression, generalized linear models (logistic, Poisson, and more), nonlinear regression, mixed-effects models for grouped or hierarchical data, and Gaussian process regression. You can perform stepwise variable selection, regularization with lasso and ridge, and multinomial logistic regression for multiclass outcomes.

The toolbox provides k-means, hierarchical clustering, Gaussian mixture models, DBSCAN, spectral clustering, and other methods to identify patterns, determine optimal cluster numbers, and detect anomalies or outliers.

The toolbox includes over 25 built-in probability distributions (including normal, Weibull, gamma, beta, lognormal, and many more) that you can fit to data using fitdist or the interactive Distribution Fitter app. You can also create and fit custom distributions using maximum likelihood estimation, compare fits using information criteria, and work with censored data for survival and reliability analyses.

You can generate portable C/C++ code for embedded deployment using MATLAB Coder, use native Simulink blocks for simulations and Model-Based Design, or compile MATLAB code with MATLAB Compiler for IT environments.

Statistics and Machine Learning Toolbox provides a unified environment, from exploratory analysis through production deployment, in a single tested and documented product. Key differentiators include interactive guided apps for beginners, C/C++ code generation for embedded deployment via MATLAB Coder, and direct Simulink integration for Model-Based Design. Open-source alternatives like Python and R offer free, community-supported options, but typically require assembling multiple packages and do not provide equivalent paths for embedded code generation or simulation integration.

You can perform t-tests, distribution tests, nonparametric tests, ANOVA, analysis of covariance, repeated measures ANOVA, and tests for autocorrelation, randomness, and distribution comparisons.

Yes. You can use interactive apps such as Distribution Fitter to fit probability distributions to your data, and Classification Learner and Regression Learner to train, compare, and validate machine learning models, all without writing code.

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