Specifies the task of the analysis. It must be one of
‘classification’, ‘regression’.
shuffle :boolean, optional (default=True)
Whether to shuffle the data.
standardize :boolean, optional (default=True)
Whether to sdandardize the data.
scoring :string, callable or None, optional, default: None
A string (see scikit-learn’s model evaluation documentation) or
a scorer callable object / function with
signature scorer(estimator, X, y).
cv :integer or cross-validation generator.
If an integer is passed, it is the number of folds (default 3).
K-fold cv (for regression task) or Stratified k-fold cv is
used by default.
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects.
n_jobs :integer, optional (default=1)
The number of jobs to run in parallel. If -1, then the number of
jobs is set to the number of cores.
random_state :int seed, RandomState instance, or None (default=0)
The seed of the pseudo random number generator
lang :string (default=’en’)
Specifies the language in the report. It must be one of
‘en’ (English), ‘jp’ (Japanese).
estimator :object type that implements the “fit” and “predict” methods
A object of that type is instantiated for each grid point.
param_grid :dict or list of dictionaries
Dictionary with parameters names (string) as keys and
lists of parameter settings to try as values, or a list of
such dictionaries, in which case the grids spanned by
each dictionary in the list are explored.
This enables searching over any sequence of parameter settings.