Compare model (experiment) summaries¶

X-axis: Feature name
Y-axis: Gain value (the importance of the feature in the model)

You can compare model summaries (experiments) to understand the similarities and differences between models (model summaries).

Instructions¶

To compare model summaries, consider the following instructions:

In the H2O Model Validation navigation menu, click Experiments.
In the experiments table, select at least two model summaries to compare.

Note

You cannot compare a model (experiment) summary if its state is not done or you have not generated a dataset summary for the dataset. To learn how to create a summary for a dataset, see Create model (experiment) summary.
Click Compare.

Note

A comparison table and a feature importance chart appear when comparing the selected model summaries. To learn more, see Comparison table and Chart: Feature importance.

Column name	Description
`Test Name`	The name of the experiment.
`Scorer`	The scorer of the experiment.
`Validation Score`	Experiment validation score value.
`Test Score`	Experiment test score value.
`Accuracy`	Experiment accuracy value.
`Time`	Experiment time value.
`Interpretability`	Experiment interpretability value.
`Task`	Experiment problem type (e.g., regression).
`Target`	Experiment target column (target feature).
`Dropped Columns`	Dropped columns that Driverless AI dropped during the experiment to not use as predictors.
`Train Data Name`	Name of the experiment train dataset.
`Train Data Shape`	The number of rows and columns in the experiment train dataset ((rows, columns)).
`Test Data Name`	Name of the experiment test dataset.
`Test Data Shape`	The number of rows and columns in the experiment test dataset ((rows, columns)).

The feature importance chart displays all the features of the compared models (model summaries).

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