multiclass_classification_objective¶
Base class for all multiclass classification objectives.
Module Contents¶
Classes Summary¶
Base class for all multiclass classification objectives. 
Contents¶

class
evalml.objectives.multiclass_classification_objective.
MulticlassClassificationObjective
[source]¶ Base class for all multiclass classification objectives.
Attributes
problem_types
[ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS]
Methods
Calculate the percent difference between scores.
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.
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.
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
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.
 Args:
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

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.

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].
 Raises
ValueError – If the inputs are malformed.

classmethod