cost_benefit_matrix

Module Contents

Classes Summary

CostBenefitMatrix

Score using a cost-benefit matrix. Scores quantify the benefits of a given value, so greater numeric

Contents

class evalml.objectives.cost_benefit_matrix.CostBenefitMatrix(true_positive, true_negative, false_positive, false_negative)[source]

Score using a cost-benefit matrix. Scores quantify the benefits of a given value, so greater numeric scores represents a better score. Costs and scores can be negative, indicating that a value is not beneficial. For example, in the case of monetary profit, a negative cost and/or score represents loss of cash flow.

Parameters

  • true_positive (float) – Cost associated with true positive predictions

  • true_negative (float) – Cost associated with true negative predictions

  • false_positive (float) – Cost associated with false positive predictions

  • false_negative (float) – Cost associated with false negative predictions

Attributes

greater_is_better

True

is_bounded_like_percentage

False

name

Cost Benefit Matrix

perfect_score

None

problem_types

[ProblemTypes.BINARY, ProblemTypes.TIME_SERIES_BINARY]

score_needs_proba

False

Methods

calculate_percent_difference

Calculate the percent difference between scores.

can_optimize_threshold

Returns a boolean determining if we can optimize the binary classification objective threshold.

decision_function

Apply a learned threshold to predicted probabilities to get predicted classes.

is_defined_for_problem_type

objective_function

Calculates cost-benefit of the using the predicted and true values.

optimize_threshold

Learn a binary classification threshold which optimizes the current objective.

positive_only

If True, this objective is only valid for positive data. Default False.

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.
classmethod calculate_percent_difference(cls, score, baseline_score)

Calculate the percent difference between scores.

Parameters

  • score (float) – A score. Output of the score method of this objective.

  • baseline_score (float) – A score. Output of the score method of this objective. In practice, this is the score achieved on this objective with a baseline estimator.

Returns

The percent difference between the scores. Note that for objectives that can be interpreted

as percentages, this will be the difference between the reference score and score. For all other objectives, the difference will be normalized by the reference score.

Return type

float

property can_optimize_threshold(cls)

Returns a boolean determining if we can optimize the binary classification objective threshold. This will be false for any objective that works directly with predicted probabilities, like log loss and AUC. Otherwise, it will be true.

decision_function(self, ypred_proba, threshold=0.5, X=None)

Apply a learned threshold to predicted probabilities to get predicted classes.

Parameters

  • ypred_proba (pd.Series, np.ndarray) – The classifier’s predicted probabilities

  • threshold (float, optional) – Threshold used to make a prediction. Defaults to 0.5.

  • X (pd.DataFrame, optional) – Any extra columns that are needed from training data.

Returns

predictions

classmethod is_defined_for_problem_type(cls, problem_type)
objective_function(self, y_true, y_predicted, X=None, sample_weight=None)[source]

Calculates cost-benefit of the using the predicted and true values.

Parameters

  • y_predicted (pd.Series) – Predicted labels

  • y_true (pd.Series) – True labels

  • X (pd.DataFrame) – Ignored.

  • sample_weight (pd.DataFrame) – Ignored.

Returns

Cost-benefit matrix score

Return type

float

optimize_threshold(self, ypred_proba, y_true, X=None)

Learn a binary classification threshold which optimizes the current objective.

Parameters

  • ypred_proba (pd.Series) – The classifier’s predicted probabilities

  • y_true (pd.Series) – The ground truth for the predictions.

  • X (pd.DataFrame, optional) – Any extra columns that are needed from training data.

Returns

Optimal threshold for this objective

positive_only(cls)

If True, this objective is only valid for positive data. Default False.

score(self, y_true, y_predicted, X=None, sample_weight=None)

Returns a numerical score indicating performance based on the differences between the predicted and actual values.

Parameters

  • y_predicted (pd.Series) – Predicted values of length [n_samples]

  • y_true (pd.Series) – Actual class labels of length [n_samples]

  • X (pd.DataFrame or np.ndarray) – Extra data of shape [n_samples, n_features] necessary to calculate score

  • sample_weight (pd.DataFrame or np.ndarray) – Sample weights used in computing objective value result

Returns

score

validate_inputs(self, y_true, y_predicted)

Validates the input based on a few simple checks.

Parameters

  • y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples]

  • y_true (pd.Series) – Actual class labels of length [n_samples]

Returns

None

binary_classification_objective fraud_cost