bigframes.ml.linear_model.LogisticRegression.score#
- LogisticRegression.score(X: DataFrame | Series | DataFrame | Series, y: DataFrame | Series | DataFrame | Series) DataFrame[source]#
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy, which is a harsh metric since you require that each label set be correctly predicted for each sample.
Note
Output matches that of the BigQuery ML.EVALUATE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#classification_models for the outputs relevant to this model type.
- Parameters:
X (bigframes.dataframe.DataFrame or bigframes.series.Series) – DataFrame of shape (n_samples, n_features). Test samples.
y (bigframes.dataframe.DataFrame or bigframes.series.Series) – DataFrame of shape (n_samples,) or (n_samples, n_outputs). True labels for X.
- Returns:
A DataFrame of the evaluation result.
- Return type: