Add 1.5 instead of 0.5

fbeta_score.py

@@ -46,7 +46,7 @@ _KWARGS_DESCRIPTION = """
 Args:
     predictions (`list` of `int`): Predicted labels.
     references (`list` of `int`): Ground truth labels.
-    beta (`float`): Determines the weight of recall in the combined score. Defaults to 0.5
+    beta (`float`): Determines the weight of recall in the combined score. Defaults to 1.5
     labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
     pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
     average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.