SVR#
The Support Vector Machine Regressor (SVR) is a maximum margin algorithm for the purposes of regression. Similarly to the SVC, the model produced by SVR depends only on a subset of the training data, because the cost function for building the model ignores any training data close to the model prediction given by parameter epsilon. Thus, the value of epsilon defines a margin of tolerance where no penalty is given to errors.
Note
This estimator requires the SVM extension which uses the libsvm engine under the hood.
Interfaces: Estimator, Learner
Data Type Compatibility: Continuous
Parameters#
| # | Name | Default | Type | Description |
|---|---|---|---|---|
| 1 | c | 1.0 | float | The parameter that defines the width of the margin used to separate the classes. |
| 2 | epsilon | 0.1 | float | Specifies the margin within which no penalty is associated in the training loss. |
| 3 | kernel | RBF | Kernel | The kernel function used to operate in higher dimensions. |
| 4 | shrinking | true | bool | Should we use the shrinking heuristic? |
| 5 | tolerance | 1e-3 | float | The minimum change in the cost function necessary to continue training. |
| 6 | cache size | 100.0 | float | The size of the kernel cache in MB. |
Additional Methods#
Save the model data to the filesystem:
public save(string $path) : void
Load the model data from the filesystem:
public load(string $path) : void
Example#
use Rubix\ML\Regressors\SVR;
use Rubix\ML\Kernels\SVM\RBF;
$estimator = new SVR(1.0, 0.03, new RBF(), true, 1e-3, 256.0);