Plot partial dependencies for a variable across multiple models
Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.
h2o.pd_multi_plot( object, newdata, column, best_of_family = TRUE, target = NULL, row_index = NULL, max_levels = 30 )
object |
Either a list of H2O models/model_ids or an H2OAutoML object. |
newdata |
An H2OFrame. |
column |
A feature column name to inspect. Character string. |
best_of_family |
If TRUE, plot only the best model of each algorithm family; if FALSE, plot all models. Defaults to TRUE. |
target |
If multinomial, plot PDP just for |
row_index |
Optional. Calculate Individual Conditional Expectation (ICE) for row, |
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:
aml <- h2o.automl(y = response,
training_frame = train,
max_models = 10,
seed = 1)
# Create the partial dependence plot
pdp <- h2o.pd_multi_plot(aml, test, column = "alcohol")
print(pdp)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.