pipefitter.model_selection.HyperParameterTuning.gridsearch¶
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HyperParameterTuning.
gridsearch
(table, n_jobs=None)¶ Fit model over various permutations of parameters
Parameters: table : data set
The data set to use for training and scoring
n_jobs : int
The number of jobs to run in parallel (when supported by the backend)
Returns: DataFrame
Notes
For small data sets when n_jobs > 1, the overhead of creating threads and multiple sessions on the backend may be greater than the time it takes to run each step sequentially.
Examples
Using a dict of parameter lists:
>>> hpt = HyperParameterTuning(estimator=estimator, ... param_grid = dict( ... max_depth=[6, 10], ... leaf_size=[3, 5] ... )) >>> scores = hpt.gridsearch(data)
Using a list of parameter dictionaries:
>>> hpt = HyperParameterTuning(estimator=estimator, ... param_grid = [ ... dict(max_depth=6, leaf_size=3), ... dict(max_depth=6, leaf_size=5), ... dict(max_depth=10, leaf_size=3), ... dict(max_depth=10, leaf_size=5), ... ]) >>> scores = hpt.gridsearch(data)