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. 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, shallow neural nets, k-means, and other clustering methods. You can apply interpretability techniques such as partial dependence plots, Shapley values and LIME, and automatically generate C/C++ code for embedded deployment. Native Simulink blocks let you use predictive models with simulations and Model-Based design. Many toolbox algorithms can be used on data sets that are too big to be stored in memory.
Descriptive Statistics and Visualization
Explore data through statistical plotting with interactive and visual graphics and descriptive statistics. Understand and describe potentially large sets of data quickly using descriptive statistics, including measures of central tendency, dispersion, shape, correlation, and covariance.
Identify patterns and features by applying k-means, hierarchical, DBSCAN and other clustering methods, and dividing data into groups or clusters. Determine the optimal number of clusters for the data using different evaluation criteria. Detect anomalies to identify outliers and novelties.
Assign sample variance to different sources and determine whether the variation arises within or among different population groups. Use one-way, two-way, multiway, multivariate, and nonparametric ANOVA, as well as analysis of covariance (ANOCOVA) and repeated measures analysis of variance (RANOVA).
Dimensionality Reduction and Feature Extraction
Automatically identify the subset of features that provide the best predictive power for machine learning from images, signals, text, and numeric data. Iteratively explore and create new features and select the ones that optimize performance. Reduce dimensionality by transforming existing features into new predictor variables where less descriptive features can be dropped.
Statistically analyze effects and data trends. Design experiments to create and test practical plans for how to manipulate data inputs to generate information about their effects on data outputs. Visualize and analyze time-to-failure data with and without censoring and monitor and assess the quality of industrial processes.
Generate portable and readable C/C++ code for inference of classification and regression models, descriptive statistics, and probability distributions. Generate C/C++ prediction code with reduced precision, and update parameters of deployed models without regenerating the prediction code.