RegressionSVM Predict
Libraries:
Statistics and Machine Learning Toolbox /
Regression
Description
The RegressionSVM Predict block predicts responses using an SVM regression
object (RegressionSVM
or CompactRegressionSVM
).
Import a trained SVM regression object into the block by specifying the name of a workspace variable that contains the object. The input port x receives an observation (predictor data), and the output port yfit returns a predicted response for the observation.
Examples
Ports
Input
Output
Parameters
Block Characteristics
Data Types |
|
Direct Feedthrough |
|
Multidimensional Signals |
|
Variable-Size Signals |
|
Zero-Crossing Detection |
|
More About
Tips
If you are using a linear SVM model and it has many support vectors, then prediction can be slow. To efficiently predict responses based on a linear SVM model, remove the support vectors from the
RegressionSVM
orCompactRegressionSVM
object by usingdiscardSupportVectors
.
Alternative Functionality
You can use a MATLAB Function block with the predict
object function of an SVM regression object (RegressionSVM
or CompactRegressionSVM
). For an example, see
Predict Class Labels Using MATLAB Function Block.
When deciding whether to use the RegressionSVM Predict block in the
Statistics and Machine Learning Toolbox™ library or a MATLAB Function block with the predict
function, 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
Version History
Introduced in R2020bSee Also
Blocks
- RegressionTree Predict | RegressionEnsemble Predict | RegressionNeuralNetwork Predict | RegressionGP Predict | ClassificationSVM Predict