# predict

Predict responses using support vector machine regression model

## Syntax

## Description

## Input Arguments

`Mdl`

— SVM regression model

`RegressionSVM`

object | `CompactRegressionSVM`

object

SVM regression model, specified as a `RegressionSVM`

model or a `CompactRegressionSVM`

model,
returned by `fitrsvm`

or `compact`

,
respectively.

`X`

— Predictor data used to generate responses

numeric matrix | table

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

Each row of `X`

corresponds to one observation,
and each column corresponds to one variable.

For a numeric matrix:

The variables making up the columns of

`X`

must have the same order as the predictor variables that trained`Mdl`

.If you trained

`Mdl`

using a table (for example,`Tbl`

), then`X`

can be a numeric matrix if`Tbl`

contains all numeric predictor variables. To treat numeric predictors in`Tbl`

as categorical during training, identify categorical predictors using the`CategoricalPredictors`

name-value pair argument of`fitrsvm`

. If`Tbl`

contains heterogeneous predictor variables (for example, numeric and categorical data types) and`X`

is a numeric matrix, then`predict`

throws an error.

For a table:

`predict`

does not support multicolumn variables or cell arrays other than cell arrays of character vectors.If you trained

`Mdl`

using a table (for example,`Tbl`

), then all predictor variables in`X`

must have the same variable names and data types as those that trained`Mdl`

(stored in`Mdl.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 (response variables, observation weights, etc.), but`predict`

ignores them.If you trained

`Mdl`

using a numeric matrix, then the predictor names in`Mdl.PredictorNames`

and corresponding predictor variable names in`X`

must be the same. To specify predictor names during training, see the`PredictorNames`

name-value pair argument of`fitrsvm`

. All predictor variables in`X`

must be numeric vectors.`X`

can contain additional variables (response variables, observation weights, etc.), but`predict`

ignores them.

If you set `'Standardize',true`

in `fitrsvm`

to
train `Mdl`

, then the software standardizes the columns
of `X`

using the corresponding means in `Mdl.Mu`

and
standard deviations in `Mdl.Sigma`

.

**Data Types: **`table`

| `double`

| `single`

## Output Arguments

`yfit`

— Predicted responses

vector

Predicted responses, returned as a vector of length *n*, where
*n* is the number of observations in the training
data.

For details about how to predict responses, see Equation 1 and Equation 2 in Understanding Support Vector Machine Regression.

## Examples

### Predict Test Sample Response for SVM Regression Model

Load the `carsmall`

data set. Consider a model that predicts a car's fuel efficiency given its horsepower and weight. Determine the sample size.

```
load carsmall
tbl = table(Horsepower,Weight,MPG);
N = size(tbl,1);
```

Partition the data into training and test sets. Hold out 10% of the data for testing.

rng(10); % For reproducibility cvp = cvpartition(N,'Holdout',0.1); idxTrn = training(cvp); % Training set indices idxTest = test(cvp); % Test set indices

Train a linear SVM regression model. Standardize the data.

Mdl = fitrsvm(tbl(idxTrn,:),'MPG','Standardize',true);

`Mdl`

is a `RegressionSVM`

model.

Predict responses for the test set.

YFit = predict(Mdl,tbl(idxTest,:));

Create a table containing the observed response values and the predicted response values side by side.

table(tbl.MPG(idxTest),YFit,'VariableNames',... {'ObservedValue','PredictedValue'})

`ans=`*10×2 table*
ObservedValue PredictedValue
_____________ ______________
14 9.4833
27 28.938
10 7.765
28 27.155
22 21.054
29 31.484
24.5 30.306
18.5 19.12
32 28.225
28 26.632

## Tips

If

`mdl`

is a cross-validated`RegressionPartitionedSVM`

model, use`kfoldPredict`

instead of`predict`

to predict new response values.

## Alternative Functionality

### Simulink Block

To integrate the prediction of an SVM regression model into Simulink^{®}, you can use the RegressionSVM
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 RegressionSVM 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.

This function fully supports tall arrays. For more information, see Tall Arrays.

### C/C++ Code Generation

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

Usage notes and limitations:

You can generate C/C++ code for both

`predict`

and`update`

by using a coder configurer. Or, generate code only for`predict`

by using`saveLearnerForCoder`

,`loadLearnerForCoder`

, and`codegen`

.Code generation for

`predict`

and`update`

— Create a coder configurer by using`learnerCoderConfigurer`

and then generate code by using`generateCode`

. Then you can update model parameters in the generated code without having to regenerate the code.Code generation for

`predict`

— 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`

(MATLAB Coder) to generate code for the entry-point function.

For single-precision code generation, use standardized data by specifying

`'Standardize',true`

when you train the model. To generate single-precision C/C++ code for`predict`

, specify the name-value argument`"DataType","single"`

when you call the`loadLearnerForCoder`

function.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 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™.

This table contains notes about the arguments of

`predict`

. Arguments not included in this table are fully supported.Argument Notes and Limitations `Mdl`

For the usage notes and limitations of the model object, see Code Generation of the

`CompactRegressionSVM`

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.In the coder configurer workflow,

`X`

must be a single-precision or double-precision matrix.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).

For more information, see Introduction to Code Generation.

## Version History

**Introduced in R2015b**

## See Also

`RegressionSVM`

| `CompactRegressionSVM`

| `fitrsvm`

| `kfoldPredict`

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