Class RegressionMetrics (2.12.0)

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

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

Attributes

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.

builtins.object > proto.message.Message > RegressionMetrics

__delattr__(key)

Delete the value on the given field.

This is generally equivalent to setting a falsy value.

__eq__(other)

Return True if the messages are equal, False otherwise.

__ne__(other)

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.