Inheritance

Aggregate metrics for classification/classifier models. Formulti-class models, the metrics are either macro-averaged ormicro-averaged. When macro-averaged, the metrics are calculatedfor each label and then an unweighted average is taken of thosevalues. When micro-averaged, the metric is calculated globallyby counting the total number of correctly predicted rows.

Evaluation metrics for binary classification/classifiermodels.

Evaluation metrics for clustering models.

Indicates the method to split input data into multipletables.

Distance metric used to compute the distance between twopoints.

Evaluation metrics of a model. These are either computed onall training data or just the eval data based on whether evaldata was used during training. These are not present forimported models.

The abstract base class for a message.

Indicates the learning rate optimization strategy to use.

Loss metric to evaluate model training performance.

Indicates the type of the Model.

Evaluation metrics for multi-class classification/classifiermodels.

Indicates the optimization strategy used for training.

Evaluation metrics for regression and explicit feedback typematrix factorization models.

Information about a single training query run for the model.

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.

Set the value on the given field.

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