ML.TRIAL_INFO function is used to display information regarding trials
from a hyperparameter tuning model.
For information about supported model types of each SQL statement and function, and all supported SQL statements and functions for each model type, read End-to-end user journey for each model.
model_name is the name of the model you're evaluating. If you do
not have a default project configured, prepend the project ID to the model
name in following format:
\`[PROJECT_ID].[DATASET].[MODEL]\` (including the
backticks); for example, `myproject.mydataset.mymodel`.
This statement returns in one row per trial with the following columns:
- trial_id (
int64_value): 1-based ID assigned to each trial in the approximate order of trial execution.
- hyperparameters (
STRUCT): The hyperparameters used in each trial contained in a STRUCT.
- hparam_tuning_evaluation_metrics (
STRUCT): The evaluation metrics specified by hparam_tuning_objectives. Metrics are calculated from the evaluation data. See data split section for a comparison of evaluation and test data.
- training_loss (
float64_value): The loss of the trial during the last iteration calculated using the training data.
- eval_loss (
float64_value): The loss of the trial during the last iteration calculated using the evaluation data.
- status (
string_value): The final status of the trial:
SUCCEEDED: The trial succeeded.
FAILED: The trial failed.
INFEASIBLE: The trial was not run due to an invalid combination of hyperparameters.
- error_message (
string_value): The error message if the trial did not succeed. See hyperparameter tuning error handling for more information.
- is_optimal (
bool_value): Whether the trial had the best objective value. If multiple trials are marked as optimal, then the trial with the smallest trial_id as the default trial during model serving is used.