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