package sklearn

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package sklearn
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Module Model_selection.ParameterGridSource

Sourcetype t
Sourceval of_pyobject : Py.Object.t -> t
Sourceval to_pyobject : t -> Py.Object.t
Sourceval create : param_grid:Py.Object.t -> unit -> t

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.

Read more in the :ref:`User Guide <grid_search>`.

Parameters ---------- param_grid : dict of string 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.

Sourceval get_item : ind:int -> t -> Py.Object.t

Get the parameters that would be ``ind``th in iteration

Parameters ---------- ind : int The iteration index

Returns ------- params : dict of string to any Equal to list(self)ind

Sourceval to_string : t -> string

Print the object to a human-readable representation.

Sourceval show : t -> string

Print the object to a human-readable representation.

Sourceval pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.