RegressionPartitionedModel - Cross-validated regression model - MATLAB (original) (raw)

Cross-validated regression model

Description

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 andY with the first 1/5 excluded, the second model stored inTrained{2} was trained on X andY with the second 1/5 excluded, and so on. When you callkfoldPredict, 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 bykfoldPredict using the model trained without this observation.

Creation

Description

You can create a RegressionPartitionedModel object in two ways:

• Create a cross-validated model from a regression tree model objectRegressionTree by using thecrossval object function.
• Create a cross-validated model by using the fitrtree function and specifying one of the name-value argumentsCrossVal, CVPartition,Holdout, KFold, orLeaveout.

Properties

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This property is read-only.

Data Types: cell

This property is read-only.

Categorical predictor indices, specified as a vector of positive integers. CategoricalPredictors contains index values indicating that the corresponding predictors are categorical. The index values are between 1 and p, where p is the number of predictors used to train the model. If none of the predictors are categorical, then this property is empty ([]).

Data Types: single | double

This property is read-only.

Name of the cross-validated model, returned as a character vector.

Data Types: char

Parameters of the cross-validated model, returned as an object.

This property is read-only.

Number of observations in the training data, returned as a positive integer.NumObservations can be less than the number of rows of input data when there are missing values in the input data or response data.

Data Types: double

This property is read-only.

Partition used in cross-validation, returned as a CVPartition object.

This property is read-only.

Predictor names in order of their appearance in the predictor dataX, specified as a cell array of character vectors. The length ofPredictorNames is equal to the number of columns in X.

Data Types: cell

Response variable name, specified as a character vector.

Data Types: char

Function for transforming the raw response values (mean squared error), specified as a function handle or 'none'. The default 'none' means no transformation; equivalently, 'none' means @(x)x. A function handle must accept a matrix of response values and return a matrix of the same size.

Add or change a ResponseTransform function using dot notation:

tree.ResponseTransform = @function

Data Types: char | function_handle

Trained learners, returned as a cell array of compact regression models.

Data Types: cell

This property is read-only.

Scaled weights in the ensemble, returned as a numeric vector. W has length n, the number of rows in the training data. The sum of the elements of W is 1.

Data Types: double

This property is read-only.

Predictor values, returned as a real matrix or table. Each column ofX represents one variable (predictor), and each row represents one observation.

Data Types: double | table

This property is read-only.

Class labels corresponding to the observations in X, returned as a categorical array, cell array of character vectors, character array, logical vector, or numeric vector. Each row of Y represents the classification of the corresponding row of X.

Data Types: single | double | logical | char | string | cell | categorical

Object Functions

Examples

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Load the sample data. Create a variable X containing the Horsepower and Weight data.

load carsmall X = [Horsepower Weight];

Construct a regression tree using the sample data.

cvtree = fitrtree(X,MPG,'crossval','on');

Evaluate the cross-validation error of the carsmall data using Horsepower and Weight as predictor variables for mileage (MPG).

Extended Capabilities

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Usage notes and limitations:

• RegressionPartitionedModel can be a cross-validated regression tree trained by using fitrtree with GPU array input arguments.
• The object functions of a RegressionPartitionedModel model fully support GPU arrays.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2011a