Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.
h2o.ice_plot(model, newdata, column, target = NULL, max_levels = 30)
model | An H2OModel. |
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newdata | An H2OFrame. |
column | A feature column name to inspect. |
target | If multinomial, plot PDP just for |
max_levels | An integer specifying the maximum number of factor levels to show. Defaults to 30. |
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: gbm <- h2o.gbm(y = response, training_frame = train) # Create the individual conditional expectations plot ice <- h2o.ice_plot(gbm, test, column = "alcohol") print(ice) # }