Hello,
I was wondering how exactly the hyperparameter optimization works in this example: Example. The default setting is 5-fold cross-validation, but the output is a normal RegressionSVM and not a RegressionPartitionedSVM. That's how I understand the process, please give me feedback.
Let´s consider the first step of the hyperparameter optimization. The algorithm choses a initial hyperparameter setting and learns a model with 4/5 of the data. Now it evaluates the performance on the 1/5 of the data. What happens next? Is this hyperparameter setting used again and one model learned on another of the 4/5 data? After 5 iterations you now have 5 objectiv function values which are used for the calculation of the loss? This loss is the final loss for the first hyperparameter setting. This procedure is now repeated 30 times?
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