R/explain.R
h2o.pd_multi_plot.Rd
Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.
h2o.pd_multi_plot( object, newdata, column, best_of_family = TRUE, target = NULL, row_index = NULL, max_levels = 30, show_rug = TRUE )
object | Either a list of H2O models/model_ids or an H2OAutoML object. |
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newdata | An H2OFrame. |
column | A feature column name to inspect. Character string. |
best_of_family | If TRUE, plot only the best model of each algorithm family; if FALSE, plot all models. Defaults to TRUE. |
target | If multinomial, plot PDP just for |
row_index | Optional. Calculate Individual Conditional Expectation (ICE) for row, |
max_levels | An integer specifying the maximum number of factor levels to show. Defaults to 30. |
show_rug | Show rug to visualize the density of the column. Defaults to TRUE. |
A ggplot2 object
# 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 partial dependence plot pdp <- h2o.pd_multi_plot(aml, test, column = "alcohol") print(pdp) # }