h2o (version 3.32.0.1)

h2o.ice_plot: Plot Individual Conditional Expectation (ICE) for each decile

Description

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.

Usage

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

Run this code
# 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)
# }

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