DeepcheckScorer#

class DeepcheckScorer[source]#

Encapsulate scorer function with extra methods.

Scorer functions are functions used to compute various performance metrics, using the model and data as inputs, rather than the labels and predictions. Scorers are callables with the signature scorer(model, features, y_true). Additional data on scorer functions can be found at https://scikit-learn.org/stable/modules/model_evaluation.html.

Parameters

scorert.Union[str, t.Callable]: sklearn scorer name or callable
namestr: scorer name

__init__(scorer: Union[str, Callable], name: str)[source]#

__new__(*args, **kwargs)#

Methods

`DeepcheckScorer.filter_nulls`(dataset)	Return data of dataset without null labels.
`DeepcheckScorer.is_negative_scorer`()	If initialized as sklearn scorer name, return whether it's negative scorer.
`DeepcheckScorer.score_perfect`(dataset)	Calculate the perfect score of the current scorer for given dataset.
`DeepcheckScorer.validate_fitting`(model, ...)	Validate given scorer for the model and dataset.

ModelType

DeepcheckScorer.filter_nulls