evalml.tuners.GridSearchTuner

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

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