predict

Predict responses using regression XGBoost model

Since R2026a

Syntax

Yfit = predict(mdl,X)

Yfit = predict(mdl,X,UseParallel=UseParallel)

Description

Yfit = predict(mdl,X) returns a vector of predicted responses for the predictor data in the table or matrix X, based on the pretrained XGBoost regression model mdl.

Yfit = predict(mdl,X,UseParallel=UseParallel) specifies whether to perform computations in parallel.

Examples

Predict a Response Using an XGBoost Model

Import a pretrained XGBoost regression model trained using the carsmall dataset to predict the fuel economy (MPG) of a car. The pretrained model is provided with this example and was trained in Python using Cylinders, Displacement, Horsepower, and Weight as predictors.

load carsmall
modelfile = "trainedRegressionXGBoostModel.json";
Mdl = importModelFromXGBoost(modelfile)

Mdl = 
  CompactRegressionXGBoost
               ResponseName: 'Y'
          ResponseTransform: 'none'
                 NumTrained: 30
    ImportedModelParameters: [1×1 struct]


  Properties, Methods

Predict the MPG for a car with 4 cylinders, 200 cubic inch engine displacement, 150 horsepower, and that weighs 3000 lbs.

X0 = [4 200 150 3000];
predict(Mdl,X0)

ans = single

24.0842

The XGBoost model predicts the car's efficiency to be 24.08 mpg.

Input Arguments

mdl — Compact regression XGBoost model

CompactRegressionXGBoost model object

Compact regression XGBoost model, specified as a CompactRegressionXGBoost model object created with importModelFromXGBoost.

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. If there are missing values in a row, the software uses the learned branch direction from the pretrained model. The predictor data cannot include categorical predictors (logical, categorical, char, string, or cell).

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 mdl.

For a table:

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

• All predictor variables in X must have the same variable names and data types as those stored in mdl.PredictorNames. X can contain additional variables, such as response variables and observation weights, but predict ignores them.

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 XGBoost model mdl.

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.

• Only single-precision C/C++ code is supported.

• Fixed-point code generation is not supported.

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

  Argument	Notes and Limitations
  X	For general code generation, X must be a single-precision matrix. The number of rows, or observations, in X can be a variable size, but the number of columns in X must be fixed.

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:

• You cannot use UseParallel with GPU arrays.

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

Version History

Introduced in R2026a