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Version: v0.14.0

View a model (experiment) summary

A model summary contains an array of summary metrics about a Driverless AI (DAI) experiment in your established DAI connection (e.g., important features, scorer, validation score, etc.).


To access a model summary, consider the following instructions:

  1. In the H2O Model Validation navigation menu, click Experiments.
    • On the Experiments card, in particular, in the experiments table you can view a model summary for all your Driverless AI (DAI) experiments (models) in your established DAI connection. To learn more, see Experiments table columns.
    • The experiment summaries in the Experiments card are for the experiments found in the established DAI connection. To learn more, see Create a connection.
  2. In the experiments table, select the model summary you want to view.
  3. Click View.

Experiments table columns

Column nameDescription
NameExperiment name.
Model summaryState of model summary. See Model summary states to learn about all the different states for the model summary column.
ScorerExperiment scorer.
Val scoreExperiment validation score value.
Test scoreExperiment test score value.
Train dataset nameName of the experiment train dataset.
Test dataset nameName of the experiment test dataset.
Adversarial SimilarityThe number of Adversarial Similarity tests complete and scheduled to run.
BacktestingThe number of Backtesting tests complete and scheduled to run.
Drift DetectionThe number of Drift Detection tests complete and scheduled to run.
Size Dependency The number of Size Dependency tests complete and scheduled to run.

Model summary states

As follows are the different types of states a model summary can be in:

  • NotCreated
    • H2O Model Validation has not created the model summary.
  • Created
    • H2O Model Validation created the model summary.
  • Running
    • H2O Model Validation is currently running the model summary.
  • Done
    • H2O Model Validation has completed the model summary.
  • Deleted
    • H2O Model Validation deleted the model summary.
  • Error
    • An error occurred during the model summary.
  • Timeout
    • There was not enough time to complete the model summary.


Graph: Tuning process

The tuning process graph displays the iteration data of the experiment.

  • X-axis: Iteration (since Driverless AI is an AutoML tool, it has several iterations before the final modelling pipeline.)
  • Y-axis: Depends on the scorer of the experiment (e.g., RMSE, AUC)


Chart: Feature importance

The feature importance chart displays the top features of the experiment.

  • X-axis: Gain value (the feature's importance to the model)
  • Y-axis: Feature name