dlpy.metrics.f1_score¶
-
dlpy.metrics.
f1_score
(y_true, y_pred, pos_label, castable=None, id_vars=None)¶ Compute the f1 score of the binary classification task. f1 score is defined as :math:`
- rac{2PR}{P+R}`, where \(P\) is the precision and \(R\) is
the recall.
- Parameters
- y_truestring or
CASColumn
The column of the ground truth labels. If it is a string, then y_pred has to be a string and they both belongs to the same CASTable specified by the castable argument. If it is a
CASColumn
, then y_pred has to be aCASColumn
, and the castable argument is ignored. When both y_pred and y_true areCASColumn
, they can be in different CASTables.- y_predstring or
CASColumn
The column of the predicted class labels. If it is a string, then y_true has to be a string and they both belongs to the same CASTable specified by the castable argument. If it is a
CASColumn
, then y_true has to be aCASColumn
, and the castable argument is ignored. When both y_pred and y_true areCASColumn
, they can be in different CASTables.- pos_labelstring, int or float
The positive class label.
- castable
CASTable
, optional The CASTable object to use as the source if the y_pred and y_true are strings. Default = None
- id_varsstring or list of strings, optional
Column names that serve as unique id for y_true and y_pred if they are from different CASTables. The column names need to appear in both CASTables, and they serve to match y_true and y_pred appropriately, since observation orders can be shuffled in distributed computing environment. Default = None.
- y_predstring or
- y_truestring or
- Returns
- scorefloat