resubPredict

Predict response of regression ensemble by resubstitution

Syntax

Yfit = resubPredict(ens)

Yfit = resubPredict(ens,Name=Value)

Description

Yfit = resubPredict(ens) returns a vector of ens.X elements containing the responses predicted by ens for the data ens.X. Yfit contains the predictions of ens on the data used by fitrensemble to create ens.

Yfit = resubPredict(ens,Name=Value) specifies additional options using one or more name-value arguments. For example, you can specify the indices of weak learners for predicting responses, and whether to perform computations in parallel.

Examples

Find Mean-Squared Error of Resubstitution Predictions

Find the resubstitution predictions of mileage from the carsmall data, and look at their mean-squared difference from the training data.

Load the carsmall data set and select horsepower and vehicle weight as predictors.

load carsmall
X = [Horsepower Weight];

Train an ensemble of regression trees.

ens = fitrensemble(X,MPG,Method="LSBoost",Learners="Tree");

Find the resubstitution predictions of MPG.

Yfit = resubPredict(ens);

Calculate the mean-squared difference of the resubstitution predictions from the training data.

MSE = mean((Yfit - ens.Y).^2)

MSE = 
0.5836

Confirm that the result is the same as the result of resubLoss.

resubLoss(ens)

ans = 
0.5836

Input Arguments

ens — Regression ensemble model

RegressionEnsemble model object | RegressionBaggedEnsemble model object

Regression ensemble model, specified as a RegressionEnsemble or RegressionBaggedEnsemble model object trained with fitrensemble.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: resubPredict(ens,Learners=[1 2 3 5],UseParallel=true) specifies to use the first, second, third, and fifth learners in the ensemble, and to perform computations in parallel.

Learners — Indices of weak learners

[1:ens.NumTrained] (default) | vector of positive integers

Indices of the weak learners in the ensemble to use with resubPredict, specified as a vector of positive integers in the range [1:ens.NumTrained]. By default, the function uses all learners.

Example: Learners=[1 2 4]

Data Types: single | double

UseParallel — Flag to run in parallel

false or 0 (default) | true or 1

Flag to run in parallel, specified as a numeric or logical 1 (true) or 0 (false). If you specify UseParallel=true, the resubPredict function executes for-loop iterations by using parfor. The loop runs in parallel when you have Parallel Computing Toolbox™.

Example: UseParallel=true

Data Types: logical

Output Arguments

Yfit — Predicted response values

numeric column vector

Predicted response values, returned as a numeric column vector with the same number of rows as X. Each row of Yfit gives the predicted response to the corresponding row of X, based on the regression model ens.

Extended Capabilities

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run in parallel, set the UseParallel name-value argument to true in the call to this function.

For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).

You cannot use UseParallel with GPU arrays.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• You cannot use UseParallel with GPU arrays.

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

Version History

Introduced in R2011a