Plot Individual Conditional Expectation (ICE) for each decile

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)

Arguments

model	An H2OModel.
newdata	An H2OFrame.
column	A feature column name to inspect.
target	If multinomial, plot PDP just for target category. Character string.
max_levels	An integer specifying the maximum number of factor levels to show. Defaults to 30.

Value

A ggplot2 object

Examples

# 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)
# }