Analyze and model data using statistics and machine learning
Statistics Toolbox™ provides statistical and machine learning algorithms and tools for organizing, analyzing, and modeling data. You can use regression or classification for predictive modeling, generate random numbers for Monte Carlo simulations, use statistical plots for exploratory data analysis, and perform hypothesis tests.
For analyzing multidimensional data, Statistics Toolbox includes algorithms that let you identify key variables that impact your model with sequential feature selection, transform your data with principal component analysis, apply regularization and shrinkage, or use partial least squares regression. The toolbox provides supervised and unsupervised machine learning algorithms, including boosted and bagged decision trees, K-means and hierarchical clustering, K-nearest neighbor search, Gaussian mixtures, the expectation maximization algorithm, and hidden Markov models. With these algorithms you can extract meaning from your data and develop predictive models.