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.
Discover machine learning capabilities in MATLAB® for classification, regression, clustering, and deep learning, including apps for automated model training and code generation.
Workflow for training, comparing and improving classification models, including automated, manual, and parallel training.
Workflow for training, comparing and improving regression models, including automated, manual, and parallel training.
Compare data distributions using median, interquartile range, and percentiles.
Visually compare the empirical distribution of sample data with a specified distribution.
Generate random samples from specified probability distributions, and displays display the samples as histograms.
Understand the steps for supervised learning and the characteristics of nonparametric classification and regression functions.
Address statistical modeling problems with active data collection.