evalml.tuners.GridSearchTuner

class evalml.tuners.GridSearchTuner(space, n_points=10, random_state=0)

Grid Search Optimizer

Example

>>> tuner = GridSearchTuner([(1,10), ['A', 'B']], n_points=5)
>>> print(tuner.propose())
(1.0, 'A')
>>> print(tuner.propose())
(1.0, 'B')
>>> print(tuner.propose())
(3.25, 'A')

Methods

__init__	Generate all of the possible points to search for in the grid
add	Not applicable to grid search tuner as generated parameters are not dependent on scores of previous parameters.
is_search_space_exhausted	Checks if it is possible to generate a set of valid parameters.
propose	Returns hyperparameters from _grid_points iterations