predict
Class: classreg.learning.regr.CompactRegressionSVM, RegressionSVM
Namespace: classreg.learning.regr
Predict responses using support vector machine regression model
Description
uses the yfit
= predict(Mdl
,X
,PredictionForMissingValue=prediction
)prediction
value as the predicted response for
observations with missing values in the predictor data X
. By
default, predict
uses the median of the observed response
values in the training data. (since R2023b)
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 trainedMdl
.If you trained
Mdl
using a table (for example,Tbl
), thenX
can be a numeric matrix ifTbl
contains all numeric predictor variables. To treat numeric predictors inTbl
as categorical during training, identify categorical predictors using theCategoricalPredictors
name-value pair argument offitrsvm
. IfTbl
contains heterogeneous predictor variables (for example, numeric and categorical data types) andX
is a numeric matrix, thenpredict
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 inX
must have the same variable names and data types as those that trainedMdl
(stored inMdl.PredictorNames
). However, the column order ofX
does not need to correspond to the column order ofTbl
.Tbl
andX
can contain additional variables (response variables, observation weights, etc.), butpredict
ignores them.If you trained
Mdl
using a numeric matrix, then the predictor names inMdl.PredictorNames
and corresponding predictor variable names inX
must be the same. To specify predictor names during training, see thePredictorNames
name-value pair argument offitrsvm
. All predictor variables inX
must be numeric vectors.X
can contain additional variables (response variables, observation weights, etc.), butpredict
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
prediction
— Predicted response value to use for observations with missing predictor values
"median"
(default) | "mean"
| numeric scalar
Since R2023b
Predicted response value to use for observations with missing predictor values, specified as "median"
, "mean"
, or a numeric scalar.
Value | Description |
---|---|
"median" | predict uses the median of the observed response values in the training data as the predicted response value for observations with missing predictor values. |
"mean" | predict uses the mean of the observed response values in the training data as the predicted response value for observations with missing predictor values. |
Numeric scalar | predict uses this value as the predicted response value for observations with missing predictor values. |
Example: "mean"
Example: NaN
Data Types: single
| double
| char
| string
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-validatedRegressionPartitionedSVM
model, usekfoldPredict
instead ofpredict
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.
The
predict
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
andupdate
by using a coder configurer. Or, generate code only forpredict
by usingsaveLearnerForCoder
,loadLearnerForCoder
, andcodegen
.Code generation for
predict
andupdate
— Create a coder configurer by usinglearnerCoderConfigurer
and then generate code by usinggenerateCode
. 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 usingsaveLearnerForCoder
. Define an entry-point function that loads the saved model by usingloadLearnerForCoder
and calls thepredict
function. Then usecodegen
(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 forpredict
, specify the name-value argument"DataType","single"
when you call theloadLearnerForCoder
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 bygenerateLearnerDataTypeFcn
, and then use the structure as an input argument ofloadLearnerForCoder
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 inX
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.
If the value of
PredictionForMissingValue
is nonnumeric, then it must be a compile-time constant.
For more information, see Introduction to Code Generation.
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™. (since R2023a)
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2015bR2023b: Specify predicted response value to use for observations with missing predictor values
Starting in R2023b, when you predict or compute the loss, some regression models allow you to specify the predicted response value for observations with missing predictor values. Specify the PredictionForMissingValue
name-value argument to use a numeric scalar, the training set median, or the training set mean as the predicted value. When computing the loss, you can also specify to omit observations with missing predictor values.
This table lists the object functions that support the
PredictionForMissingValue
name-value argument. By default, the
functions use the training set median as the predicted response value for observations with
missing predictor values.
Model Type | Model Objects | Object Functions |
---|---|---|
Gaussian process regression (GPR) model | RegressionGP , CompactRegressionGP | loss , predict , resubLoss , resubPredict |
RegressionPartitionedGP | kfoldLoss , kfoldPredict | |
Gaussian kernel regression model | RegressionKernel | loss , predict |
RegressionPartitionedKernel | kfoldLoss , kfoldPredict | |
Linear regression model | RegressionLinear | loss , predict |
RegressionPartitionedLinear | kfoldLoss , kfoldPredict | |
Neural network regression model | RegressionNeuralNetwork , CompactRegressionNeuralNetwork | loss , predict , resubLoss , resubPredict |
RegressionPartitionedNeuralNetwork | kfoldLoss , kfoldPredict | |
Support vector machine (SVM) regression model | RegressionSVM , CompactRegressionSVM | loss , predict , resubLoss , resubPredict |
RegressionPartitionedSVM | kfoldLoss , kfoldPredict |
In previous releases, the regression model loss
and predict
functions listed above used NaN
predicted response values for observations with missing predictor values. The software omitted observations with missing predictor values from the resubstitution ("resub") and cross-validation ("kfold") computations for prediction and loss.
R2023a: GPU array support
Starting in R2023a, predict
fully supports GPU arrays.
See Also
RegressionSVM
| CompactRegressionSVM
| fitrsvm
| kfoldPredict
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