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Statistics and Machine Learning Toolbox Product Description

Analyze and model data using statistics and machine learning

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.

Key Features

  • Regression techniques, including linear, generalized linear, nonlinear, robust, regularized, ANOVA, repeated measures, and mixed-effects models

  • Big data algorithms for dimension reduction, descriptive statistics, k-means clustering, linear regression, logistic regression, and discriminant analysis

  • Univariate and multivariate probability distributions, random and quasi-random number generators, and Markov chain samplers

  • Hypothesis tests for distributions, dispersion, and location, and design of experiments (DOE) techniques for optimal, factorial, and response surface designs

  • Classification Learner app and algorithms for supervised machine learning, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, discriminant analysis, and Gaussian process regression

  • Unsupervised machine learning algorithms, including k-means, k-medoids, hierarchical clustering, Gaussian mixtures, and hidden Markov models

  • Bayesian optimization for tuning machine learning algorithms by searching for optimal hyperparameters

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