evalml.objectives.Precision

class evalml.objectives.Precision[source]

Precision score for binary classification.

name = 'Precision'
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 = False

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

Inheritance diagram of Precision
evalml.objectives.MCCMulticlass.validate_inputs evalml.objectives.Precision.__init__