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Linear, generalized linear, nonlinear, and nonparametric techniques for supervised learning

Regression models describe the relationship between a response (output) variable, and one or more predictor (input) variables. Statistics and Machine Learning Toolbox™ allows you to fit linear, generalized linear, and nonlinear regression models, including stepwise models and mixed-effects models. Once you fit a model, you can use it to predict or simulate responses, assess the model fit using hypothesis tests, or use plots to visualize diagnostics, residuals, and interaction effects.

Statistics and Machine Learning Toolbox also provides nonparametric regression methods to accommodate more complex regression curves without specifying the relationship between the response and the predictors with a predetermined regression function. You can predict responses for new data using the trained model. Gaussian process regression models also enable you to compute prediction intervals.

Click to go to the example, Linear Mixed-Effects Model Workflow.