Predict responses of linear regression model (2024)

ypred = predict(mdl,Xnew)

[ypred,yci]= predict(mdl,Xnew)

[ypred,yci]= predict(mdl,Xnew,Name,Value)

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: [ypred,yci] = predict(Mdl,Xnew,'Alpha',0.01,'Simultaneous',true) returns the confidence interval yci with a 99% confidence level, computed simultaneously for all predictor values.

• 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 the name-value argument "DataType","single" when you call the loadLearnerForCoder function.

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

  Argument	Notes and Limitations
  mdl	Suppose you train a linear model by using fitlm and specifying 'RobustOpts' as a structure with an anonymous function handle for the RobustWgtFun field, use saveLearnerForCoder to save the model, and then use loadLearnerForCoder to load the model. In this case, loadLearnerForCoder cannot restore the Robust property into the MATLAB® Workspace. However, loadLearnerForCoder can load the model at compile time within an entry-point function for code generation. For the usage notes and limitations of the model object, see Code Generation of the CompactLinearModel object.
  Xnew	Xnew must be a single-precision or double-precision matrix or a table containing numeric variables, categorical variables, or both. The number of rows, or observations, in Xnew can be a variable size, but the number of columns in Xnew must be fixed. If you want to specify Xnew as a table, then your model must be trained using a table, and you must ensure that your entry-point function for prediction: Accepts data as arrays Creates a table from the data input arguments and specifies the variable names in the table Passes 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 pair arguments	Names in name-value arguments must be compile-time constants. For example, to allow a user-defined significance level in the generated code, include {coder.Constant('Alpha'),0} in the -args value of codegen (MATLAB Coder).