dlpy.metrics.explained_variance_score¶

dlpy.metrics.explained_variance_score(y_true, y_pred, castable=None, id_vars=None)¶

Compute the explained variance score for a regression task. It is the fraction of the target variable variance that is explained by the model.

Parameters:

y_true : string or CASColumn: The column of the ground truth target values. 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 target values. 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
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: