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)
model |
An H2OModel. |
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)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.