lead_scoring#

Lead scoring objective.

Module Contents#

Classes Summary#

LeadScoring

Lead scoring.

Contents#

class evalml.objectives.lead_scoring.LeadScoring(true_positives=1, false_positives=-1)[source]#

Lead scoring.

Parameters

true_positives (int) – Reward for a true positive. Defaults to 1.
false_positives (int) – Cost for a false positive. Should be negative. Defaults to -1.

Attributes

expected_range	None
greater_is_better	True
is_bounded_like_percentage	False
name	Lead Scoring
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`	Returns whether or not an objective is defined for a problem type.
`objective_function`	Calculate the profit per lead.
`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, y_train=None, X=None, sample_weight=None)[source]#

Calculate the profit per lead.

Parameters

y_predicted (pd.Series) – Predicted labels.
y_true (pd.Series) – True labels.
y_train (pd.Series) – Ignored.
X (pd.DataFrame) – Ignored.
sample_weight (pd.DataFrame) – Ignored.

Returns

Profit per lead

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.

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.