Vlad

# Cleaner code: folds = optunity.cross_validation.generate_folds(data.shape[0], num_folds=3) # use one fold: outer_cv = optunity.cross_validated(x=data, y=labels, num_folds=1, folds=[folds[0:1]], aggregator=optunity.cross_validation.mean) # compute area under ROC curve of default parameters def compute_roc_standard(x_train, y_train,...

I run a computationally intensive learning (deep neural network). I would like sample biger parameter space in expense of replication (multiple folds) of each parameter space point

Could you please point to the relevant tutorial/documentation?