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BinarizeTargetClassifier

galaxy_ml.binarize_target._binarize_estimators.BinarizeTargetClassifier(classifier, z_score=-1, value=None, less_is_positive=True, verbose=0)

Convert continuous target to binary labels (True and False) and apply a classification estimator.

Parameters

• classifier: object
  Estimator object such as derived from sklearn ClassifierMixin.
• z_score: float, default=-1.0
  Threshold value based on z_score. Will be ignored when fixed_value is set
• value: float, default=None
  Threshold value
• less_is_positive: boolean, default=True
  When target is less the threshold value, it will be converted to True, False otherwise.
• verbose: int, default=0
  If greater than 0, print discretizing info.

Attributes

• classifier_: object
  Fitted classifier
• discretize_value: float
  The threshold value used to discretize True and False targets

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BinarizeTargetRegressor

galaxy_ml.binarize_target._binarize_estimators.BinarizeTargetRegressor(regressor, z_score=-1, value=None, less_is_positive=True, verbose=0)

Extend regression estimator to have discretize_value

Parameters

• regressor: object
  Estimator object such as derived from sklearn RegressionMixin.
• z_score: float, default=-1.0
  Threshold value based on z_score. Will be ignored when value is set
• value: float, default=None
  Threshold value
• less_is_positive: boolean, default=True
  When target is less the threshold value, it will be converted to True, False otherwise.
• verbose: int, default=0
  If greater than 0, print discretizing info.

Attributes

• regressor_: object
  Fitted regressor
• discretize_value: float
  The threshold value used to discretize True and False targets

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BinarizeTargetTransformer

galaxy_ml.binarize_target._binarize_estimators.BinarizeTargetTransformer(transformer, z_score=-1, value=None, less_is_positive=True)

Extend transformaer to work for binarized target.

Parameters

• transformer: object
  Estimator object such as derived from sklearn TransformerMixin, including feature_selector and preprocessor
• z_score: float, default=-1.0
  Threshold value based on z_score. Will be ignored when fixed_value is set
• value: float, default=None
  Threshold value
• less_is_positive: boolean, default=True
  When target is less the threshold value, it will be converted to True, False otherwise.

Attributes

• transformer_: object
  Fitted regressor
• discretize_value: float
  The threshold value used to discretize True and False targets

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[source]

_BinarizeTargetThresholdScorer

sklearn.metrics.scorer._BinarizeTargetThresholdScorer(score_func, sign, kwargs)

Base class to make binarized target specific scorer.