skada.metrics.ImportanceWeightedScorer
- class skada.metrics.ImportanceWeightedScorer(weight_estimator=None, scoring=None, greater_is_better=True, kwargs=None)[source]
Score based on source data using sample weight.
See [17] for details.
- Parameters:
- weight_estimatorestimator object, optional
The estimator to use to estimate the densities of source and target observations. If None, a KernelDensity estimator is with default parameters used.
- scoringstr or callable, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature
scorer(estimator, X, y). If None, the provided estimator object's score method is used.- greater_is_betterbool, default=True
Whether scorer is a score function (default), meaning high is good, or a loss function, meaning low is good. In the latter case, the scorer object will sign-flip the outcome of the scorer.
- Attributes:
- weight_estimator_source_object
The estimator object fitted on the source data.
- weight_estimator_target_object
The estimator object fitted on the target data.
References
[17]Masashi Sugiyama et al. Covariate Shift Adaptation by Importance Weighted Cross Validation. Journal of Machine Learning Research, 2007.
