Train Regression Ensemble
This example shows how to create a regression ensemble to predict mileage of cars based on their horsepower and weight, trained on the carsmall
data.
Load the carsmall
data set.
load carsmall
Prepare the predictor data.
X = [Horsepower Weight];
The response data is MPG
. The only available boosted regression ensemble type is LSBoost
. For this example, arbitrarily choose an ensemble of 100 trees, and use the default tree options.
Train an ensemble of regression trees.
Mdl = fitrensemble(X,MPG,'Method','LSBoost','NumLearningCycles',100)
Mdl = RegressionEnsemble ResponseName: 'Y' CategoricalPredictors: [] ResponseTransform: 'none' NumObservations: 94 NumTrained: 100 Method: 'LSBoost' LearnerNames: {'Tree'} ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.' FitInfo: [100x1 double] FitInfoDescription: {2x1 cell} Regularization: []
Plot a graph of the first trained regression tree in the ensemble.
view(Mdl.Trained{1},'Mode','graph');
By default, fitrensemble
grows shallow trees for LSBoost.
Predict the mileage of a car with 150 horsepower weighing 2750 lbs.
mileage = predict(Mdl,[150 2750])
mileage = 23.6713