Generate Model Explanations
The H2O Explainability Interface is a convenient wrapper to a number of explainabilty methods and visualizations in H2O. The function can be applied to a single model or group of models and 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( object, newdata, columns = NULL, top_n_features = 5, include_explanations = "ALL", exclude_explanations = NULL, plot_overrides = NULL )
object |
One of the following: an H2O model, a list of H2O models, an H2OAutoML object or an H2OAutoML Leaderboard slice. |
newdata |
An H2OFrame. |
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 of outputs with class "H2OExplanation"
## 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(aml, test)
print(exa)
# Create the explanation for the leader model
exm <- h2o.explain(aml@leader, test)
print(exm)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.