multiclass_classification_objective

Module Contents

Classes Summary

MulticlassClassificationObjective

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_percent_difference

Calculate the percent difference between scores.

greater_is_better

Returns a boolean determining if a greater score indicates better model performance.

is_bounded_like_percentage

Returns whether this objective is bounded between 0 and 1, inclusive.

is_defined_for_problem_type

name

Returns a name describing the objective.

objective_function

Computes the relative value of the provided predictions compared to the actual labels, according a specified metric

perfect_score

Returns the score obtained by evaluating this objective on a perfect model.

positive_only

If True, this objective is only valid for positive data. Default False.

score

Returns a numerical score indicating performance based on the differences between the predicted and actual values.

score_needs_proba

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

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 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)
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

Arguments:

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. Default 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]

Returns

None