Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.model_selection`.ParameterGrid

class sklearn.model_selection.ParameterGrid(param_grid)

  Grid 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. The order of the generated parameter combinations is deterministic.

  Read more in the User Guide.

  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.

  See also

  `GridSearchCV`

    Uses `ParameterGrid` to perform a full parallelized parameter search.

  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