The H2O Explainability Interface is a convenient wrapper to a number of explainabilty methods and visualizations in H2O. The function can be applied to a single model or group of models and returns a list of explanations, which are individual units of explanation such as a partial dependence plot or a variable importance plot. Most of the explanations are visual (ggplot plots). These plots can also be created by individual utility functions as well.
h2o.explain( object, newdata, columns = NULL, top_n_features = 5, include_explanations = "ALL", exclude_explanations = NULL, plot_overrides = NULL )
| object | A list of H2O models, an H2O AutoML instance, or an H2OFrame with a 'model_id' column (e.g. H2OAutoML leaderboard). |
|---|---|
| newdata | An H2OFrame. |
| columns | A vector of column names or column indices to create plots with. If specified parameter top_n_features will be ignored. |
| top_n_features | An integer specifying the number of columns to use, ranked by variable importance (where applicable). |
| include_explanations | If specified, return only the specified model explanations. (Mutually exclusive with exclude_explanations) |
| exclude_explanations | Exclude specified model explanations. |
| plot_overrides | Overrides for individual model explanations, e.g.
|
List of outputs with class "H2OExplanation"
# NOT RUN { library(h2o) h2o.init() # Import the wine dataset into H2O: f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv" df <- h2o.importFile(f) # Set the response response <- "quality" # Split the dataset into a train and test set: splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1) train <- splits[[1]] test <- splits[[2]] # Build and train the model: aml <- h2o.automl(y = response, training_frame = train, max_models = 10, seed = 1) # Create the explanation for whole H2OAutoML object exa <- h2o.explain(aml, test) print(exa) # Create the explanation for the leader model exm <- h2o.explain(aml@leader, test) print(exm) # }