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# RegressionPartitionedEnsemble class

Superclasses: RegressionPartitionedModel

Cross-validated regression ensemble

## Description

RegressionPartitionedEnsemble is a set of regression ensembles trained on cross-validated folds. Estimate the quality of classification by cross validation using one or more "kfold" methods: kfoldfun, kfoldLoss, or kfoldPredict. 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.

## Construction

cvens = crossval(ens) creates a cross-validated ensemble from ens, a regression ensemble. For syntax details, see the crossval method reference page.

cvens = fitensemble(X,Y,method,nlearn,learners,name,value) creates a cross-validated ensemble when name is one of 'crossval', 'kfold', 'holdout', 'leaveout', or 'cvpartition'. For syntax details, see the fitensemble function reference page.

### Input Arguments

 ens A regression ensemble constructed with fitensemble.

## Properties

 CategoricalPredictors List of categorical predictors. CategoricalPredictors is a numeric vector with indices from 1 to p, where p is the number of columns of X. CrossValidatedModel Name of the cross-validated model, a string. Kfold Number of folds used in a cross-validated tree, a positive integer. ModelParams Object holding parameters of tree. NObservations Numeric scalar containing the number of observations in the training data. NTrainedPerFold Vector of Kfold elements. Each entry contains the number of trained learners in this cross-validation fold. Partition The partition of class cvpartition used in creating the cross-validated ensemble. PredictorNames A cell array of names for the predictor variables, in the order in which they appear in X. ResponseName Name of the response variable Y, a string. ResponseTransform Function handle for transforming scores, or string representing a built-in transformation function. 'none' means no transformation; equivalently, 'none' means @(x)x. Add or change a ResponseTransform function by dot addressing: `ens.ResponseTransform = @function` Trainable Cell array of ensembles trained on cross-validation folds. Every ensemble is full, meaning it contains its training data and weights. Trained Cell array of compact ensembles trained on cross-validation folds. W The scaled weights, a vector with length n, the number of rows in X. X A matrix of predictor values. Each column of X represents one variable, and each row represents one observation. Y A numeric column vector with the same number of rows as X. Each entry in Y is the response to the data in the corresponding row of X.

## Methods

 kfoldLoss Cross-validation loss of partitioned regression ensemble resume Resume training ensemble

### Inherited Methods

 kfoldfun Cross validate function kfoldLoss Cross-validation loss of partitioned regression model kfoldPredict Predict response for observations not used for training.

## Copy Semantics

Value. To learn how value classes affect copy operations, see Copying Objects in the MATLAB® documentation.

## Examples

Construct a partitioned regression ensemble, and examine the cross-validation losses for the folds:

```load carsmall
XX = [Cylinders Displacement Horsepower Weight];
YY = MPG;
rens = fitensemble(XX,YY,'LSBoost',100,'Tree');
cvrens = crossval(rens);
L = kfoldLoss(cvrens,'mode','individual')

L =
42.4468
12.3158
65.9432
39.0019
30.5908
16.6225
17.3071
46.1769
8.0561
12.9689```