predict

Predict responses using regression ensemble model

Syntax

Yfit = predict(ens,X)

Yfit = predict(ens,X,Name=Value)

Description

Yfit = predict(ens,X) returns predicted responses to the predictor data in the table or matrix X, based on the regression ensemble model ens.

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

Examples

Predict Responses Based on Regression Ensemble

Find the predicted mileage for a car based on regression ensemble trained on the carsmall data.

Load the carsmall data set and select the number of cylinders, engine displacement, horsepower, and vehicle weight as predictors.

load carsmall
X = [Cylinders Displacement Horsepower Weight];

Train an ensemble of regression trees and predict MPG for a four-cylinder car, with 200 cubic inch engine displacement, 150 horsepower, weighing 3000 lbs.

rens = fitrensemble(X,MPG);
Mileage = predict(rens,[4 200 150 3000])

Mileage = 
25.6467

Input Arguments

ens — Regression ensemble model

RegressionEnsemble model object | RegressionBaggedEnsemble model object | CompactRegressionEnsemble model object

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

X — Predictor data

numeric matrix | table

Predictor data used to predict responses, specified as a numeric matrix or a table.

Each row of X corresponds to one observation, and each column corresponds to one variable.

For a numeric matrix:

• The variables that make up the columns of X must have the same order as the predictor variables used to train ens.

• If you trained ens using a table (for example, Tbl), X can be a numeric matrix if Tbl contains only numeric predictor variables. To treat numeric predictors in Tbl as categorical during training, specify categorical predictors using the CategoricalPredictors name-value argument of fitrensemble. If Tbl contains heterogeneous predictor variables (for example, numeric and categorical data types) and X is a numeric matrix, predict issues an error.

For a table:

• predict does not support multicolumn variables or cell arrays other than cell arrays of character vectors.

• If you trained ens using a table (for example, Tbl), all predictor variables in X must have the same variable names and data types as those used to train ens (stored in ens.PredictorNames). However, the column order of X does not need to correspond to the column order of Tbl. Tbl and X can contain additional variables, such as response variables and observation weights, but predict ignores them.

• If you trained ens using a numeric matrix, the predictor names in ens.PredictorNames must be the same as the corresponding predictor variable names in X. To specify predictor names during training, use the PredictorNames name-value argument of fitrensemble. All predictor variables in X must be numeric vectors. X can contain additional variables, such as response variables and observation weights, but predict ignores them.

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: predict(ens,X,Learners=[1 2 3 5],UseParallel=true) specifies to use the first, second, third, and fifth learners in the ensemble ens, 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 predict, 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

UseObsForLearner — Option to use observations for learners

true(N,T) (default) | logical matrix

Option to use observations for learners, specified as a logical matrix of size N-by-T, where:

• N is the number of rows of X.

• T is the number of weak learners in ens.

When UseObsForLearner(i,j) is true (default), learner j is used in predicting the response of row i of X.

Example: UseObsForLearner=logical([1 1; 0 1; 1 0])

Data Types: logical matrix

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 predict 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.

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 Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.

• Support for variable-size 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 post-processing before or after predictions in the same MATLAB Function block.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

Usage notes and limitations:

• You cannot use the UseParallel name-value argument with tall arrays.

For more information, see Tall Arrays.

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• Use saveLearnerForCoder, loadLearnerForCoder, and codegen (MATLAB Coder) to generate code for the predict function. Save a trained model by using saveLearnerForCoder. Define an entry-point function that loads the saved model by using loadLearnerForCoder and calls the predict function. Then use codegen to generate code for the entry-point function.

• To generate single-precision C/C++ code for predict, specify DataType="single" when you call the loadLearnerForCoder function.

• When you generate single- or double-precision C/C++ code for ensembles consisting of all tree learners or all discriminant learners, the generated code of predict uses parfor (MATLAB Coder) to create loops that run in parallel on supported shared-memory multicore platforms. If your compiler does not support the Open Multiprocessing (OpenMP) application interface or you disable OpenMP library, MATLAB Coder™ treats the parfor-loops as for-loops. To find supported compilers, see Supported Compilers. To disable OpenMP library, set the EnableOpenMP property of the configuration object to false. For details, see coder.CodeConfig (MATLAB Coder). (since R2026a)

• You can also generate fixed-point C/C++ code for predict. Fixed-point code generation requires an additional step that defines the fixed-point data types of the variables required for prediction. Create a fixed-point data type structure by using the data type function generated by generateLearnerDataTypeFcn, and then use the structure as an input argument of loadLearnerForCoder in an entry-point function. Generating fixed-point C/C++ code requires MATLAB Coder and Fixed-Point Designer™.

• Generating fixed-point code for predict includes propagating data types for individual learners and, therefore, can be time consuming.

• This table contains notes about the arguments of predict. Arguments not included in this table are fully supported.

  Argument	Notes and Limitations
  ens	For the usage notes and limitations of the model object, see Code Generation of the CompactRegressionEnsemble object.
  X	For general code generation, X must be a single-precision or double-precision matrix or a table containing numeric variables, categorical variables, or both. For fixed-point code generation, X must be a fixed-point matrix. The number of rows, or observations, in X can be a variable size, but the number of columns in X must be fixed. If you want to specify X as a table, then your model must be trained using a table, and your entry-point function for prediction must do the following: Accept data as arrays. Create a table from the data input arguments and specify the variable names in the table. Pass the table to predict. For an example of this table workflow, see Generate Code to Classify Data in Table. For more information on using tables in code generation, see Code Generation for Tables (MATLAB Coder) and Table Limitations for Code Generation (MATLAB Coder).
  Name-value arguments	Names in name-value arguments must be compile-time constants. For example, to allow user-defined indices up to 5 weak learners in the generated code, include {coder.Constant('Learners'),coder.typeof(0,[1,5],[0,1])} in the -args value of codegen (MATLAB Coder).
  Learners	For fixed-point code generation, the Learners value must have an integer data type.

For more information, see Introduction to Code Generation for Statistics and Machine Learning Functions.

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 tall or GPU arrays or in code generation.

GPU Arrays

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

Usage notes and limitations:

• The predict function does not support ensembles trained using decision tree learners with surrogate splits.

• 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

R2026a: Multithreading support for C/C++ code generation of ensembles of trees and ensembles of discriminant analysis models (requires MATLAB Coder)

When you generate single- or double-precision C/C++ code for ensembles consisting of all tree learners or all discriminant analysis learners, the generated code of predict uses parfor (MATLAB Coder) to create loops that run in parallel on supported shared-memory multicore platforms. For more information, see Code Generation.