What is Cross Validation on Multiple Kernel Learning in shogun

In this recipe, we shall learn what is Cross Validation on Multiple Kernel Learning in shogun with the help of an example.

Recipe Objective: What is Cross-Validation on Multiple Kernel Learning in shogun?

This recipe explains what is Cross-Validation on Multiple Kernel Learning in shogun.
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Cross-Validation on Multiple Kernel Learning

Cross-Validation is a statistical method of evaluating and comparing learning algorithms by dividing data into two segments: one is used to learn or train a model, and the other is used to validate the model. In typical cross-validation, the training and validation sets must cross over in successive rounds such that each data point has a chance of being validated against.

z = LibSVM()
x = MKLClassification(z)
x.set_interleaved_optimization_enabled(False)
x.set_kernel(kernel)

split = StratifiedCrossValidationSplitting(labels, 5)
acc = AccuracyMeasure()
c = CrossValidation(x, combined_features, labels, split, acc)
c.set_autolock(False)
c.set_num_runs(2)

c.subscribe_to_parameters(ParameterObserverCV(True))
res = CrossValidationResult()
res = CrossValidationResult.obtain_from_generic(c.evaluate())

o = mkl_obs.get_observation(0)
f = obs.get_fold(0)
m = MKLClassification.obtain_from_generic(f.get_trained_machine())

k = CombinedKernel.obtain_from_generic(m.get_kernel())
w = k.get_subkernel_weights()

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