Regression
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.
统计一个d 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.
- Regression Learner App
Interactively train, validate, and tune regression models - Linear Regression
Multiple, stepwise, multivariate regression models, and more - Generalized Linear Models
Logistic regression, multinomial regression, Poisson regression, and more - Nonlinear Regression
Nonlinear fixed- and mixed-effects regression models - Support Vector Machine Regression
Support vector machines for regression models - Gaussian Process Regression
Gaussian process regression models (kriging) - Regression Trees
Binary decision trees for regression - Regression Tree Ensembles
Random forests, boosted and bagged regression trees - 模型构建和评估
Feature selection, hyperparameter optimization, cross-validation, residual diagnostics, plots