Add info about GAM to missing_values_handling topic

[exalate-issue-sync]

{{missing_values_handing}} is supported in GAM, but information about GAM is missing from the description in the Parameter Appendix entry:

"This option is used to specify the way that the algorithm will treat missing values. In H2O, the Deep Learning and GLM algorithms will either skip or mean-impute rows with NA values. The GLM algorithm can also use plug_values, which allows you to specify a single-row frame containing values that will be used to impute missing values of the training/validation frame. Both algorithms default to MeanImputation. Note that in Deep Learning, unseen categorical variables are imputed by adding an extra “missing” level. In GLM, unseen categorical levels are replaced by the most frequent level present in training (mod)."

[h2o-ops]

JIRA Issue Migration Info

Jira Issue: PUBDEV-7526
Assignee: hannah.tillman
Reporter: Angela Bartz
State: Resolved
Fix Version: 3.30.0.4
Attachments: N/A
Development PRs: Available

Linked PRs from JIRA

#4611