Class RegressionMetrics (3.25.0)

RegressionMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Evaluation metrics for regression and explicit feedback type matrix factorization models.


Name Description
mean_absolute_error google.protobuf.wrappers_pb2.DoubleValue
Mean absolute error.
mean_squared_error google.protobuf.wrappers_pb2.DoubleValue
Mean squared error.
mean_squared_log_error google.protobuf.wrappers_pb2.DoubleValue
Mean squared log error.
median_absolute_error google.protobuf.wrappers_pb2.DoubleValue
Median absolute error.
r_squared google.protobuf.wrappers_pb2.DoubleValue
R^2 score. This corresponds to r2_score in ML.EVALUATE.




Delete the value on the given field.

This is generally equivalent to setting a falsy value.



Return True if the messages are equal, False otherwise.



Return True if the messages are unequal, False otherwise.


__setattr__(key, value)

Set the value on the given field.

For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.