fraud_cost#

Score the percentage of money lost of the total transaction amount process due to fraud.

Module Contents#

Classes Summary#

FraudCost

Score the percentage of money lost of the total transaction amount process due to fraud.

Contents#

class evalml.objectives.fraud_cost.FraudCost(retry_percentage=0.5, interchange_fee=0.02, fraud_payout_percentage=1.0, amount_col='amount')[source]#

Score the percentage of money lost of the total transaction amount process due to fraud.

Parameters

retry_percentage (float) – What percentage of customers that will retry a transaction if it is declined. Between 0 and 1. Defaults to 0.5.
interchange_fee (float) – How much of each successful transaction you pay. Between 0 and 1. Defaults to 0.02.
fraud_payout_percentage (float) – Percentage of fraud you will not be able to collect. Between 0 and 1. Defaults to 1.0.
amount_col (str) – Name of column in data that contains the amount. Defaults to “amount”.

Attributes

expected_range	None
greater_is_better	False
is_bounded_like_percentage	True
name	Fraud Cost
perfect_score	0.0
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`	Returns whether or not an objective is defined for a problem type.
`objective_function`	Calculate amount lost to fraud per transaction given predictions, true values, and dataframe with transaction amount.
`optimize_threshold`	Learn a binary classification threshold which optimizes the current objective.
`positive_only`	If True, this objective is only valid for positive data. Defaults to False.
`score`	Returns a numerical score indicating performance based on the differences between the predicted and actual values.
`validate_inputs`	Validate inputs for scoring.

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.

Returns: Whether or not an objective can be optimized.
Return type: bool

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)#: Returns whether or not an objective is defined for a problem type.

objective_function(self, y_true, y_predicted, X, sample_weight=None)[source]#

Calculate amount lost to fraud per transaction given predictions, true values, and dataframe with transaction amount.

Parameters

y_predicted (pd.Series) – Predicted fraud labels.
y_true (pd.Series) – True fraud labels.
X (pd.DataFrame) – Data with transaction amounts.
sample_weight (pd.DataFrame) – Ignored.

Returns

Amount lost to fraud per transaction.

Return type

float

Raises

ValueError – If amount_col is not a valid column in the input data.

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.

Raises

RuntimeError – If objective cannot be optimized.

positive_only(cls)#: If True, this objective is only valid for positive data. Defaults to 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)#: Validate inputs for scoring.

cost_benefit_matrix

lead_scoring