Generate Model Explanations for a single row

Explain the behavior of a model or group of models with respect to a single row of data. The function returns a list of explanations, which are individual units of explanation such as a partial dependence plot or a variable importance plot. Most of the explanations are visual (ggplot plots). These plots can also be created by individual utility functions as well.

h2o.explain_row(
  object,
  newdata,
  row_index,
  columns = NULL,
  top_n_features = 5,
  include_explanations = "ALL",
  exclude_explanations = NULL,
  plot_overrides = NULL,
  background_frame = NULL
)

Arguments

object	A list of H2O models, an H2O AutoML instance, or an H2OFrame with a 'model_id' column (e.g. H2OAutoML leaderboard).
newdata	An H2OFrame.
row_index	A row index of the instance to explain.
columns	A vector of column names or column indices to create plots with. If specified parameter top_n_features will be ignored.
top_n_features	An integer specifying the number of columns to use, ranked by variable importance (where applicable).
include_explanations	If specified, return only the specified model explanations. (Mutually exclusive with exclude_explanations)
exclude_explanations	Exclude specified model explanations.
plot_overrides	Overrides for individual model explanations, e.g., list(shap_explain_row = list(columns = 5))
background_frame	Optional frame, that is used as the source of baselines for the marginal SHAP. Setting it enables calculating SHAP in more models but it can be more time and memory consuming.

Value

List of outputs with class "H2OExplanation"

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:
aml <- h2o.automl(y = response,
                  training_frame = train,
                  max_models = 10,
                  seed = 1)

# Create the explanation for whole H2OAutoML object
exa <- h2o.explain_row(aml, test, row_index = 1)
print(exa)

# Create the explanation for the leader model
exm <- h2o.explain_row(aml@leader, test, row_index = 1)
print(exm)
# }