package sklearn
Module Model_selection.ParameterGridSource
val create :
[ `Grid of (string * Dict.param_grid) list
| `Grids of (string * Dict.param_grid) list list ] ->
tGrid of parameters with a discrete number of values for each.
Can be used to iterate over parameter value combinations with the Python built-in function iter.
Read more in the :ref:`User Guide <grid_search>`.
Parameters ---------- param_grid : dict of str to sequence, or sequence of such The parameter grid to explore, as a dictionary mapping estimator parameters to sequences of allowed values.
An empty dict signifies default parameters.
A sequence of dicts signifies a sequence of grids to search, and is useful to avoid exploring parameter combinations that make no sense or have no effect. See the examples below.
Examples -------- >>> from sklearn.model_selection import ParameterGrid >>> param_grid = 'a': [1, 2], 'b': [True, False] >>> list(ParameterGrid(param_grid)) == ( ... {'a': 1, 'b': True}, {'a': 1, 'b': False}, ... {'a': 2, 'b': True}, {'a': 2, 'b': False}) True
>>> grid = {'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]} >>> list(ParameterGrid(grid)) == {'kernel': 'linear'}, ... {'kernel': 'rbf', 'gamma': 1}, ... {'kernel': 'rbf', 'gamma': 10} True >>> ParameterGrid(grid)1 == 'kernel': 'rbf', 'gamma': 1 True
See also -------- :class:`GridSearchCV`: Uses :class:`ParameterGrid` to perform a full parallelized parameter search.
Get the parameters that would be ``ind``th in iteration
Parameters ---------- ind : int The iteration index
Returns ------- params : dict of str to any Equal to list(self)ind
Iterate over the points in the grid.
Returns ------- params : iterator over dict of str to any Yields dictionaries mapping each estimator parameter to one of its allowed values.
Pretty-print the object to a formatter.