evalml.objectives.AUC

class evalml.objectives.AUC

AUC score for binary classification.

name = 'AUC'

greater_is_better = True

perfect_score = 1.0

positive_only = False

problem_types = [<ProblemTypes.BINARY: 'binary'>, <ProblemTypes.TIME_SERIES_BINARY: 'time series binary'>]

score_needs_proba = True

Methods

__init__	Initialize self.
calculate_percent_difference	Calculate the percent difference between scores.
decision_function	Apply a learned threshold to predicted probabilities to get predicted classes.
is_defined_for_problem_type
objective_function	Computes the relative value of the provided predictions compared to the actual labels, according a specified metric
optimize_threshold	Learn a binary classification threshold which optimizes the current objective.
score	Returns a numerical score indicating performance based on the differences between the predicted and actual values.
validate_inputs	Validates the input based on a few simple checks.

Class Inheritance

evalml.objectives.AccuracyMulticlass.validate_inputs evalml.objectives.AUC.__init__