fraud_cost#
Score the percentage of money lost of the total transaction amount process due to fraud.
Module Contents#
Classes Summary#
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 the percent difference between scores.
Returns a boolean determining if we can optimize the binary classification objective threshold.
Apply a learned threshold to predicted probabilities to get predicted classes.
Returns whether or not an objective is defined for a problem type.
Calculate amount lost to fraud per transaction given predictions, true values, and dataframe with transaction amount.
Learn a binary classification threshold which optimizes the current objective.
If True, this objective is only valid for positive data. Defaults to False.
Returns a numerical score indicating performance based on the differences between the predicted and actual values.
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, y_train=None, 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.
y_train (pd.Series) – Ignored.
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, y_train=None, 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]
y_train (pd.Series) – Observed training values 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.