Class RegressionMetrics (3.0.1)

Version 3.0.1

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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.

Inheritance

builtins.object > proto.message.Message > RegressionMetrics

Methods

__delattr__

__delattr__(key)

Delete the value on the given field.

This is generally equivalent to setting a falsy value.

__eq__

__eq__(other)

Return True if the messages are equal, False otherwise.

__ne__

__ne__(other)

Return True if the messages are unequal, False otherwise.

__setattr__

__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.