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Cross-validated regression model
RegressionPartitionedModel is a set of regression models trained on cross-validated folds. Estimate the quality of regression by cross validation using one or more "kfold" methods: kfoldPredict, kfoldLoss, and kfoldfun. Every "kfold" method uses models trained on in-fold observations to predict response for out-of-fold observations. For example, suppose you cross validate using five folds. In this case, every training fold contains roughly 4/5 of the data and every test fold contains roughly 1/5 of the data. The first model stored in Trained{1} was trained on X and Y with the first 1/5 excluded, the second model stored in Trained{2} was trained on X and Y with the second 1/5 excluded, and so on. When you call kfoldPredict, it computes predictions for the first 1/5 of the data using the first model, for the second 1/5 of data using the second model and so on. In short, response for every observation is computed by kfoldPredict using the model trained without this observation.
cvmodel = crossval(tree) creates a cross-validated classification model from a regression tree. For syntax details, see the crossval method reference page.
cvmodel = fitrtree(X,Y,Name,Value) creates a cross-validated model when name is one of 'CrossVal', 'KFold', 'Holdout', 'Leaveout', or 'CVPartition'. For syntax details, see the fitrtree function reference page.
tree |
A regression tree constructed with fitrtree. |
kfoldfun | Cross validate function |
kfoldLoss | Cross-validation loss of partitioned regression model |
kfoldPredict | Predict response for observations not used for training. |
Value. To learn how value classes affect copy operations, see Copying Objects in the MATLAB^{®} documentation.
ClassificationPartitionedModel | RegressionPartitionedEnsemble