evalml.objectives.LeadScoring

class evalml.objectives.LeadScoring(true_positives=1, false_positives=- 1)

Lead scoring.

name = 'Lead Scoring'

greater_is_better = True

perfect_score = inf

positive_only = False

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

score_needs_proba = False

Methods

__init__	Create instance.
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	Calculate the profit per lead.
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.FraudCost.validate_inputs evalml.objectives.LeadScoring.__init__