binary_classification_objective#
Base class for all binary classification objectives.
Module Contents#
Classes Summary#
Base class for all binary classification objectives. |
Contents#
- class evalml.objectives.binary_classification_objective.BinaryClassificationObjective[source]#
Base class for all binary classification objectives.
Attributes
problem_types
[ProblemTypes.BINARY, ProblemTypes.TIME_SERIES_BINARY]
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 the expected range of the objective, which is not necessarily the possible ranges.
Returns a boolean determining if a greater score indicates better model performance.
Returns whether this objective is bounded between 0 and 1, inclusive.
Returns whether or not an objective is defined for a problem type.
Returns a name describing the objective.
Computes the relative value of the provided predictions compared to the actual labels, according a specified metric.
Learn a binary classification threshold which optimizes the current objective.
Returns the score obtained by evaluating this objective on a perfect model.
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.
Returns a boolean determining if the score() method needs probability estimates.
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)[source]#
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
- property expected_range(cls)#
Returns the expected range of the objective, which is not necessarily the possible ranges.
For example, our expected R2 range is from [-1, 1], although the actual range is (-inf, 1].
- property greater_is_better(cls)#
Returns a boolean determining if a greater score indicates better model performance.
- property is_bounded_like_percentage(cls)#
Returns whether this objective is bounded between 0 and 1, inclusive.
- classmethod is_defined_for_problem_type(cls, problem_type)#
Returns whether or not an objective is defined for a problem type.
- property name(cls)#
Returns a name describing the objective.
- abstract classmethod objective_function(cls, y_true, y_predicted, X=None, sample_weight=None)#
Computes the relative value of the provided predictions compared to the actual labels, according a specified metric.
- 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
Numerical value used to calculate score
- optimize_threshold(self, ypred_proba, y_true, X=None)[source]#
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.
- property perfect_score(cls)#
Returns the score obtained by evaluating this objective on a perfect model.
- 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
- property score_needs_proba(cls)#
Returns a boolean determining if the score() method needs probability estimates.
This should be true for objectives which work with predicted probabilities, like log loss or AUC, and false for objectives which compare predicted class labels to the actual labels, like F1 or correlation.