Skip to main content
Version: v0.15.0

Settings: Adversarial similarity

H2O Model Validation offers an array of settings for an adversarial similarity test. Below, each setting is described in turn.

Test name​

Defines the name for the validation test; by default, H2O Model Validation will assign a name to the validation test that you can rewrite.

Primary Dataset​

Defines one of the two datasets H2O Model Validation uses during the validation test to observe similar or dissimilar rows between the Primary and Secondary Datasets.

note

Models: Within the context of validating a model, the defined Primary Dataset needs to follow the structure of the model's training dataset.

Secondary Dataset​

Defines one of the two datasets H2O Model Validation uses during the validation test to observe similar or dissimilar rows between the Primary and Secondary Datasets.

note
  • The defined Primary Dataset dictates the required format of the Secondary Dataset (similar columns).
  • H2O Model Validation drops a particular column in the Secondary Dataset if that column is not present in the defined Primary Dataset.

Columns to drop​

Defines the columns H2O Model Validation drops during model training.

info

This setting is proper when you want to drop columns that cause high dissimilarity (for example, a time column).

Compute Shapley values​

Determines if H2O Model Validation computes Shapley values for the model used to analyze the similarity between the Primary and Secondary Dataset. H2O Model Validation uses the generated Shapley values to create an array of visual metrics that provide valuable insights into the contribution of individual features to the overall model performance.

note
  • Generating Shapley values for the model can lead to a significant impact on the runtime.
  • Generated visual metrics can help understand what might cause a higher degree of dissimilarity between the primary and secondary dataset. To learn more about generated visual metrics, see Metrics: Adversarial similarity.

Delete test models and datasets from the Worker after finish​

Determines if H2O Model Validation should delete the artifacts created in the Platform connected to the Worker Connection. In this case, artifacts refer to experiments and datasets generated during the adversarial similarity validation test. By default, H2O Model Validation checks this setting (enables it), and accordingly, H2O Model Validation deletes all artifacts because they are no longer needed after the validation test is complete.

Feedback