Class RegressionMetrics (3.31.0)
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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. This corresponds to r2_score in ML.EVALUATE.

Methods

__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.