Regressor

PoniardRegressor inherits from PoniardBaseEstimator, which sets up most of the funcionality, so you should probably read the those docs first.

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PoniardRegressor

 PoniardRegressor (estimators:Union[Dict[str,sklearn.base.RegressorMixin],
                   Sequence[sklearn.base.RegressorMixin],NoneType]=None, m
                   etrics:Union[str,Dict[str,Callable],Sequence[str],NoneT
                   ype]=None, preprocess:bool=True, custom_preprocessor:Un
                   ion[NoneType,sklearn.pipeline.Pipeline,sklearn.base.Tra
                   nsformerMixin]=None, cv:Union[int,sklearn.model_selecti
                   on._split.BaseCrossValidator,sklearn.model_selection._s
                   plit.BaseShuffleSplit,Sequence]=None, verbose:int=0,
                   random_state:Optional[int]=None,
                   n_jobs:Optional[int]=None,
                   plugins:Optional[Sequence[Any]]=None, plot_options:Opti
                   onal[poniard.plot.plot_factory.PoniardPlotFactory]=None
                   )

Cross validate multiple regressors, rank them, fine tune them and ensemble them.

PoniardRegressor takes a list/dict of scikit-learn estimators and compares their performance on a list/dict of scikit-learn metrics using a predefined scikit-learn cross-validation strategy.

	Type	Default	Details
estimators	typing.Union[typing.Dict[str, sklearn.base.RegressorMixin], typing.Sequence[sklearn.base.RegressorMixin], NoneType]	None	Estimators to evaluate.
metrics	typing.Union[str, typing.Dict[str, typing.Callable], typing.Sequence[str], NoneType]	None	Metrics to compute for each estimator. This is more restrictive than sklearn’s scoring parameter, as it does not allow callable scorers. Single strings are cast to lists automatically.
preprocess	bool	True	If True, impute missing values, standard scale numeric data and one-hot or ordinal encode categorical data.
custom_preprocessor	typing.Union[NoneType, sklearn.pipeline.Pipeline, sklearn.base.TransformerMixin]	None	Preprocessor used instead of the default preprocessing pipeline. It must be able to be included directly in a scikit-learn Pipeline.
cv	typing.Union[int, sklearn.model_selection._split.BaseCrossValidator, sklearn.model_selection._split.BaseShuffleSplit, typing.Sequence]	None	Cross validation strategy. Either an integer, a scikit-learn cross validation object, or an iterable.
verbose	int	0	Verbosity level. Propagated to every scikit-learn function and estimator.
random_state	typing.Optional[int]	None	RNG. Propagated to every scikit-learn function and estimator. The default None sets random_state to 0 so that cross_validate results are comparable.
n_jobs	typing.Optional[int]	None	Controls parallel processing. -1 uses all cores. Propagated to every scikit-learn function and estimator.
plugins	typing.Optional[typing.Sequence[typing.Any]]	None	Plugin instances that run in set moments of setup, fit and plotting.
plot_options	typing.Optional[poniard.plot.plot_factory.PoniardPlotFactory]	None	:class:poniard.plot.plot_factory.PoniardPlotFactory instance specifying Plotly format options or None, which sets the default factory.

PoniardRegressor implements PoniardClassifier._build_cv, PoniardClassifier._build_metrics and PoniardClassifier._default_estimators.

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PoniardRegressor._build_cv

 PoniardRegressor._build_cv ()

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PoniardRegressor._build_metrics

 PoniardRegressor._build_metrics ()

Build metrics.

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PoniardRegressor._default_estimators

 PoniardRegressor._default_estimators ()