FSelectorRandomSearch class that implements a simple Random Search.

In order to support general termination criteria and parallelization, we evaluate feature sets in a batch-fashion of size batch_size. Larger batches mean we can parallelize more, smaller batches imply a more fine-grained checking of termination criteria.

## Source

Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281--305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.

## Dictionary

This FSelector can be instantiated via the dictionary mlr_fselectors or with the associated sugar function fs():

### Method clone()

The objects of this class are cloneable with this method.

#### Usage

FSelectorRandomSearch\$clone(deep = FALSE)

#### Arguments

deep

Whether to make a deep clone.