This example shows how to tune hyperparameters of a regression ensemble by using hyperparameter optimization in the Regression Learner app. Compare the test set performance of the trained optimizable ensemble to that of the best-performing preset ensemble model.
In the MATLAB® Command Window, load the
carbig data set, and
create a table containing most of the variables. Separate the table into
training and test sets.
load carbig cartable = table(Acceleration,Cylinders,Displacement, ... Horsepower,Model_Year,Weight,Origin,MPG); rng('default') % For reproducibility of the data split n = length(MPG); partition = cvpartition(n,'Holdout',0.15); idxTrain = training(partition); % Indices for the training set cartableTrain = cartable(idxTrain,:); cartableTest = cartable(~idxTrain,:);
Open Regression Learner. Click the Apps tab, and then click the arrow at the right of the Apps section to open the apps gallery. In the Machine Learning and Deep Learning group, click Regression Learner.
On the Regression Learner tab, in the File section, select New Session > From Workspace.
In the New Session from Workspace dialog box, select the
cartableTrain table from the Data Set
As shown in the dialog box, the app selects the response and predictor
variables. The default response variable is
default validation option is 5-fold cross-validation, to protect against
overfitting. For this example, do not change the default settings.
To accept the default options and continue, click Start Session.
Train all preset ensemble models. On the Regression Learner tab, in the Model Type section, click the arrow to open the gallery. In the Ensembles of Trees group, click All Ensembles. In the Training section, click Train. The app trains one of each ensemble model type and displays the models in the Models pane.
If you have Parallel Computing Toolbox™, you can train all the ensemble models (All Ensembles) simultaneously by selecting the Use Parallel button in the Training section before clicking Train. After you click Train, the Opening Parallel Pool dialog box opens and remains open while the app opens a parallel pool of workers. During this time, you cannot interact with the software. After the pool opens, the app trains the ensemble models simultaneously.
The app displays a response plot for the first model (model 1.1). Blue points are true values, and yellow points are predicted values. The Models pane on the left shows the validation RMSE for each model.
Validation introduces some randomness into the results. Your model validation results can vary from the results shown in this example.
Select an optimizable ensemble model to train. On the Regression Learner tab, in the Model Type section, click the arrow to open the gallery. In the Ensembles of Trees group, click Optimizable Ensemble. The app disables the Use Parallel button when you select an optimizable model.
Select the model hyperparameters to optimize. In the Model Type section, select Advanced > Advanced. The app opens a dialog box in which you can select Optimize check boxes for the hyperparameters that you want to optimize. By default, all the check boxes are selected. For this example, accept the default selections, and click OK.
In the Training section, click Train.
The app displays a Minimum MSE Plot as it runs the optimization process. At each iteration, the app tries a different combination of hyperparameter values and updates the plot with the minimum validation mean squared error (MSE) observed up to that iteration, indicated in dark blue. When the app completes the optimization process, it selects the set of optimized hyperparameters, indicated by a red square. For more information, see Minimum MSE Plot.
The app lists the optimized hyperparameters in both the Optimization Results section to the right of the plot and the Optimized Hyperparameters section of the Current Model Summary pane.
In general, the optimization results are not reproducible.
Compare the trained preset ensemble models to the trained optimizable model. In the Models pane, the app highlights the lowest RMSE (Validation) (validation root mean squared error) by outlining it in a box. In this example, the trained optimizable ensemble outperforms the two preset models.
A trained optimizable model does not always have a lower RMSE than the trained
preset models. If a trained optimizable model does not perform well, you can try
to get better results by running the optimization for longer. In the
Model Type section, select Advanced >
Optimizer Options. In the dialog box, increase the
Iterations value. For example, you can double-click the
default value of
30 and enter a value of
60. Then click OK.
Because hyperparameter tuning often leads to overfitted models, check the performance of the optimizable ensemble model on a test set and compare it to the performance of the best preset ensemble model. Begin by importing test data into the app.
On the Regression Learner tab, in the Testing section, select Test Data > From Workspace.
In the Import Test Data dialog box, select the
cartableTest table from the Test Data
Set Variable list.
As shown in the dialog box, the app identifies the response and predictor variables.
Compute the RMSE of the best preset model and the optimizable model on the
First, in the Models pane, click the star icons next to the Bagged Trees model and the Optimizable Ensemble model.
For each model, select the model in the Models pane, and then select Test All > Test Selected in the Testing section. The app computes the test set performance of the model trained on the full data set, including training and validation data.
Sort the models based on the test set RMSE. In the Models
pane, open the Sort by list and select
In this example, the trained optimizable ensemble still outperforms the trained preset model on the test set data.