Class RegressionMetrics (3.42.3)

3.42.3 (latest) 3.42.2 3.41.0 3.40.1 3.39.0 3.38.0 3.37.0 3.36.0 3.35.1 3.34.0 3.33.0 3.31.0 3.30.0 3.29.0 3.27.0 3.26.0 3.25.0 3.24.0 3.23.1 3.22.0 3.21.0 3.20.1 3.19.0 3.18.0 3.17.2 3.16.0 3.15.0 3.14.1 3.13.0 3.12.0 3.11.4 3.4.0 3.3.6 3.2.0 3.1.0 3.0.1 2.34.4 2.33.0 2.32.0 2.31.0 2.30.1 2.29.0 2.28.1 2.27.1 2.26.0 2.25.2 2.24.1 2.23.3 2.22.1 2.21.0 2.20.0 2.19.0 2.18.0 2.17.0 2.16.1 2.15.0 2.14.0 2.13.1 2.12.0 2.11.0 2.10.0 2.9.0 2.8.0 2.7.0 2.6.2 2.5.0 2.4.0 2.3.1 2.2.0 2.1.0 2.0.0 1.28.2 1.27.2 1.26.1 1.25.0 1.24.0 1.23.1 1.22.0 1.21.0 1.20.0 1.19.0 1.18.0 1.17.0 1.16.0

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. This corresponds to r2_score in ML.EVALUATE.