pycaret.clustering.add_metric

pycaret.clustering.add_metric(id: str, name: str, score_func: type, target: str = 'pred', greater_is_better: bool = True, multiclass: bool = True, **kwargs) → Series

Adds a custom metric to be used in all functions.

id: str

Unique id for the metric.

name: str

Display name of the metric.

score_func: type

Score function (or loss function) with signature score_func(y, y_pred, **kwargs).

target: str, default = ‘pred’

The target of the score function.

• ‘pred’ for the prediction table

• ‘pred_proba’ for pred_proba

• ‘threshold’ for decision_function or predict_proba

greater_is_better: bool, default = True

Whether score_func is a score function (default), meaning high is good, or a loss function, meaning low is good. In the latter case, the scorer object will sign-flip the outcome of the score_func.

multiclass: bool, default = True

Whether the metric supports multiclass problems.

**kwargs:

Arguments to be passed to score function.

Returns:

pandas.Series