h2o.pd_plot | R Documentation |
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_plot(
object,
newdata,
column,
target = NULL,
row_index = NULL,
max_levels = 30,
binary_response_scale = c("response", "logodds"),
grouping_column = NULL,
nbins = 100,
show_rug = TRUE
)
object |
An H2O model. |
newdata |
An H2OFrame. Used to generate predictions used in Partial Dependence calculations. |
column |
A feature column name to inspect. Character string. |
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. |
binary_response_scale |
Option for binary model to display (on the y-axis) the logodds instead of the actual score. Can be one of: "response", "logodds". Defaults to "response". |
grouping_column |
A feature column name to group the data and provide separate sets of plots by grouping feature values |
nbins |
A number of bins used. Defaults to 100. |
show_rug |
Show rug to visualize the density of the column. Defaults to TRUE. |
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 partial dependence plot
pdp <- h2o.pd_plot(gbm, test, column = "alcohol")
print(pdp)
## End(Not run)
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