Support Vector Regression

Class implementing Epsilon-Support Vector Regression based on libsvm.

Constructor Parameters

  • $kernel (int) - kernel type to be used in the algorithm (default Kernel::LINEAR)
  • $degree (int) - degree of the Kernel::POLYNOMIAL function (default 3)
  • $epsilon (float) - epsilon in loss function of epsilon-SVR (default 0.1)
  • $cost (float) - parameter C of C-SVC (default 1.0)
  • $gamma (float) - kernel coefficient for ‘Kernel::RBF’, ‘Kernel::POLYNOMIAL’ and ‘Kernel::SIGMOID’. If gamma is ‘null’ then 1/features will be used instead.
  • $coef0 (float) - independent term in kernel function. It is only significant in ‘Kernel::POLYNOMIAL’ and ‘Kernel::SIGMOID’ (default 0.0)
  • $tolerance (float) - tolerance of termination criterion (default 0.001)
  • $cacheSize (int) - cache memory size in MB (default 100)
  • $shrinking (bool) - whether to use the shrinking heuristics (default true)
$regression = new SVR(Kernel::LINEAR);
$regression = new SVR(Kernel::LINEAR, $degree = 3, $epsilon=10.0);


To train a model simply provide train samples and targets values (as array). Example:

use Phpml\Regression\SVR;
use Phpml\SupportVectorMachine\Kernel;

$samples = [[60], [61], [62], [63], [65]];
$targets = [3.1, 3.6, 3.8, 4, 4.1];

$regression = new SVR(Kernel::LINEAR);
$regression->train($samples, $targets);

You can train the model using multiple data sets, predictions will be based on all the training data.


To predict sample target value use predict method. You can provide one sample or array of samples:

// return 4.03