You can use Regression Learner to train regression models to predict data. Using this app, you can explore your data, select features, specify validation schemes, train models, and assess results. You can perform automated training to search for the best regression model type, including linear regression models, regression trees, Gaussian process regression models, Support Vector Machines, and ensembles of regression trees. Perform supervised machine learning by supplying a known set of observations of input data (predictors) and known responses. Use the observations to train a model that generates predicted responses for new input data. To use the model with new data, or to learn about programmatic regression, you can export the model to the workspace or generate MATLAB code to recreate the trained model. Regression Learner includes Regression Trees. To predict a response of a regression tree, follow the tree from the root (beginning) node down to a leaf node. The leaf node contains the value of the response. Statistics and Machine Learning Toolbox trees are binary. Each step in a prediction involves checking the value of one predictor variable. For example, here is a simple regression tree. Regression trees are easy to interpret, fast for fitting and prediction, and low on memory usage. Try to grow smaller trees with fewer larger leaves to prevent overfitting. Control the leaf size with the Minimum leaf size setting. You can train ensembles of regression trees in Regression Learner. Ensemble models combine results from many weak learners into one high-quality ensemble model. You can train regression support vector machines (SVMs) in Regression Learner. Linear SVMs are easy to interpret, but can have low predictive accuracy. Nonlinear SVMs are more difficult to interpret, but can be more accurate. Support vector machine (SVM) analysis is a popular machine learning tool for classification and regression, first identified by Vladimir Vapnik and his colleagues. SVM regression is considered a nonparametric technique because it relies on kernel functions. You can train Gaussian process regression (GPR) models in Regression Learner. Neural Network Toolbox provides algorithms, pretrained models, and apps to create, train, visualize, and simulate both shallow and deep neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. This book develops the Regresion Learner techniques (linear regression models, regression trees, Gaussian process regression models, Support Vector Machines, and ensembles of regression trees), Neural Networks Regression and Generalized Linear Models (GLM). The most important content is the following: - Train Regression Models in Regression Learner App - Automated Regression Model Training - Manual Regression Model Training - Parallel Regression Model Training - Compare and Improve Regression Models - Select Data and Validation for Regression Problem - Linear Regression Models - Regression Trees - Support Vector Machines - Gaussian Process Regression Models - Ensembles of Trees - Feature Selection - Feature Transformation - Assess Model Performance - Check Performance in History List - Evaluate Model Using Residuals Plot - Export Regression Model to Predict New Data - Train Regression Trees Using Regression Learner App - Mathematical Formulation of SVM Regression - Solving the SVM Regression Optimization Problem - Fit Regression Models with a Neural Network - Multinomial Models for Nominal Responses - Multinomial Models for Ordinal Responses - Hierarchical Multinomial Models - Generalized Linear Models - Lasso Regularization of Generalized Linear Models - Regularize Poisson Regression - Regularize Logistic Regression - Regularize Wide Data in Parallel - Generalized Linear Mixed-Effects Models - Fit a Generalized Linear Mixed-Effects Model