SHAP Summary Plot

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

h2o.shap_summary_plot(
  model,
  newdata,
  columns = NULL,
  top_n_features = 20,
  sample_size = 1000,
  background_frame = NULL
)

Arguments

model	An H2O tree-based model. This includes Random Forest, GBM and XGboost only. Must be a binary classification or regression model.
newdata	An H2O Frame, used to determine feature contributions.
columns	List of columns or list of indices of columns to show. If specified, then the top_n_features parameter will be ignored.
top_n_features	Integer specifying the maximum number of columns to show (ranked by variable importance).
sample_size	Integer specifying the maximum number of observations to be plotted.
background_frame	Optional frame, that is used as the source of baselines for the marginal SHAP.

Value

A ggplot2 object

Examples

if (FALSE) {
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 SHAP summary plot
shap_summary_plot <- h2o.shap_summary_plot(gbm, test)
print(shap_summary_plot)
}