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Statistics and Machine Learning Toolbox

Analyze and model data using statistics and machine learning

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.

Predictive Maintenance with MATLAB: A Prognostics Case Study

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Paul Pilotte

What's New

From Paul Pilotte, Statistics and Machine Learning Toolbox Technical Expert