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Feature selection, hyperparameter optimization, cross-validation,
residual diagnostics, plots

When building a high-quality regression model, it is important to select the right features (or predictors), tune hyperparameters (model parameters not fit to the data), and assess model assumptions through residual diagnostics.

You can tune hyperparameters by iterating between choosing values for them, and cross-validating a model using your choices. This process yields multiple models, and the best model among them can be the one that minimizes the estimated generalization error. For example, to tune an SVM model, choose a set of box constraints and kernel scales, cross-validate a model for each pair of values, and then compare their 10-fold cross-validated mean-squared error estimates.

Certain nonparametric regression functions in Statistics and Machine Learning Toolbox™ additionally
offer automatic hyperparameter tuning through Bayesian optimization,
grid search, or random search. However, `bayesopt`

,
which is the main function to implement Bayesian optimization, is
flexible enough for many other applications. For more details, see Bayesian Optimization Workflow.

Regression Learner | Train regression models to predict data using supervised machine learning |

`sequentialfs` | Sequential feature selection |

`relieff` | Importance of attributes (predictors) using ReliefF algorithm |

`plotPartialDependence` | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |

`stepwiselm` | Create linear regression model using stepwise regression |

`stepwiseglm` | Create generalized linear regression model by stepwise regression |

`bayesopt` | Select optimal machine learning hyperparameters using Bayesian optimization |

`hyperparameters` | Variable descriptions for optimizing a fit function |

`crossval` | Loss estimate using cross validation |

`cvpartition` | Create cross validation partition for data |

`repartition` | Repartition data for cross-validation |

`test` | Test indices for cross-validation |

`training` | Training indices for cross-validation |

`coefCI` | Confidence intervals of coefficient estimates of linear model |

`coefTest` | Linear hypothesis test on linear regression model coefficients |

`dwtest` | Durbin-Watson test of linear model |

`plot` | Scatter plot or added variable plot of linear model |

`plotAdded` | Added variable plot or leverage plot for linear model |

`plotAdjustedResponse` | Adjusted response plot for linear regression model |

`plotDiagnostics` | Plot diagnostics of linear regression model |

`plotEffects` | Plot main effects of each predictor in linear regression model |

`plotInteraction` | Plot interaction effects of two predictors in linear regression model |

`plotResiduals` | Plot residuals of linear regression model |

`plotSlice` | Plot of slices through fitted linear regression surface |

`coefCI` | Confidence intervals of coefficient estimates of generalized linear model |

`coefTest` | Linear hypothesis test on generalized linear regression model coefficients |

`devianceTest` | Analysis of deviance |

`plotDiagnostics` | Plot diagnostics of generalized linear regression model |

`plotResiduals` | Plot residuals of generalized linear regression model |

`plotSlice` | Plot of slices through fitted generalized linear regression surface |

`coefCI` | Confidence intervals of coefficient estimates of nonlinear regression model |

`coefTest` | Linear hypothesis test on nonlinear regression model coefficients |

`plotDiagnostics` | Plot diagnostics of nonlinear regression model |

`plotResiduals` | Plot residuals of nonlinear regression model |

`plotSlice` | Plot of slices through fitted nonlinear regression surface |

`linhyptest` | Linear hypothesis test |

`BayesianOptimization` | Bayesian optimization results |

`optimizableVariable` | Variable description for bayesopt or other optimizers |

`cvpartition` | Data partitions for cross validation |

**Train Regression Models in Regression Learner App**

Workflow for training, comparing and improving regression models, including automated, manual, and parallel training.

**Choose Regression Model Options**

In Regression Learner, automatically train a selection of models, or compare and tune options of linear regression models, regression trees, support vector machines, Gaussian process regression models, and ensembles of regression trees.

**Feature Selection and Feature Transformation Using Regression Learner App**

Identify useful predictors using plots, manually select features to include, and transform features using PCA in Regression Learner.

**Assess Model Performance in Regression Learner App**

Compare model statistics and visualize results.

Learn about feature selection algorithms, such as sequential feature selection.

**Bayesian Optimization Workflow**

Perform Bayesian optimization using a fit function
or by calling `bayesopt`

directly.

**Variables for a Bayesian Optimization**

Create variables for Bayesian optimization.

**Bayesian Optimization Objective Functions**

Create the objective function for Bayesian optimization.

**Constraints in Bayesian Optimization**

Set different types of constraints for Bayesian optimization.

**Optimize a Boosted Regression Ensemble**

Minimize cross-validation loss of a regression ensemble.

**Bayesian Optimization Plot Functions**

Visually monitor a Bayesian optimization.

**Bayesian Optimization Output Functions**

Monitor a Bayesian optimization.

**Bayesian Optimization Algorithm**

Understand the underlying algorithms for Bayesian optimization.

**Parallel Bayesian Optimization**

How Bayesian optimization works in parallel.

**Implement Cross-Validation Using Parallel Computing**

Speed up cross-validation using parallel computing.

**Interpret Linear Regression Results**

Display and interpret linear regression output statistics.

**Examine Quality and Adjust the Fitted Model**

**Linear Regression with Interaction Effects**

Construct and analyze a linear regression model with interaction effects and interpret the results.

**Summary of Output and Diagnostic Statistics**

In linear regression, the *F*-statistic
is the test statistic for the analysis of variance (ANOVA) approach
to test the significance of the model or the components in the model.
The *t*-statistic is useful for making inferences
about the regression coefficients

**Coefficient of Determination (R-Squared)**

Coefficient of determination (R-squared) indicates
the proportionate amount of variation in the response variable *y* explained
by the independent variables *X* in the linear regression
model.

**Coefficient Standard Errors and Confidence Intervals**

Estimated coefficient variances and covariances capture the precision of regression coefficient estimates.

Residuals are useful for detecting outlying *y* values
and checking the linear regression assumptions with respect to the
error term in the regression model.

The Durbin-Watson test assesses whether there is autocorrelation among the residuals or not.

Cook's distance is useful for identifying outliers in the *X*
values (observations for predictor variables).

The hat matrix provides a measure of leverage.

Delete-1 change in covariance (`covratio`

)
identifies the observations that are influential in the regression
fit.

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