dlpy.metrics.accuracy_score

dlpy.metrics.accuracy_score(y_true, y_pred, castable=None, normalize=True, id_vars=None)

Computes the classification accuracy score.

Parameters:

y_true : string 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 a CASColumn, and the castable argument is ignored. When both y_pred and y_true are CASColumn, they can be in different CASTables.

y_pred : string 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 a CASColumn, and the castable argument is ignored. When both y_pred and y_true are CASColumn, they can be in different CASTables.

castable : CASTable, optional

The CASTable object to use as the source if the y_pred and y_true are strings. Default = None

normalize : boolean, optional

If False, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples. Default = True

id_vars : string 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

Returns:

score : float

If normalize=False, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples.