predict
Predict responses using ensemble of regression models
Description
Input Arguments

Regression ensemble created by 

Predictor data used to generate responses, specified as a numeric matrix or table. Each row of

NameValue Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue 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.

Indices of weak learners in the ensemble ranging from Default: 

A logical matrix of size
Default: 
 Indication to perform inference in parallel, specified as Default: 
Output Arguments

A numeric column vector with the same number of rows as

Examples
Alternative Functionality
Simulink Block
To integrate the prediction of an ensemble into Simulink^{®}, you can use the RegressionEnsemble
Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB^{®} Function block with the predict
function. For
examples, see Predict Responses Using RegressionEnsemble Predict Block and Predict Class Labels Using MATLAB Function Block.
When deciding which approach to use, consider the following:
If you use the Statistics and Machine Learning Toolbox library block, you can use the FixedPoint Tool (FixedPoint Designer) to convert a floatingpoint model to fixed point.
Support for variablesize arrays must be enabled for a MATLAB Function block with the
predict
function.If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or postprocessing before or after predictions in the same MATLAB Function block.