Base class for Machine Learning Detection based metrics on time series.
These metrics build a Machine Learning Classifier that learns to tell the synthetic
data apart from the real data, which later on is evaluated using Cross Validation.
The output of the metric is one minus the average ROC AUC score obtained.
Name to use when reports about this metric are printed.
The goal of this metric.
Minimum value or values that this metric can take.
Maximum value or values that this metric can take.
Initialize self. See help(type(self)) for accurate signature.
compute(real_data, synthetic_data[, …])
Compute this metric.
Recursively find subclasses of this metric.
Returns the raw_score as is, since it is already normalized.