Partial Dependence Plots

Source: R/models.R

Partial dependence plot 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. Note: Unlike randomForest's partialPlot when plotting partial dependence the mean response (probabilities) is returned rather than the mean of the log class probability.

h2o.partialPlot(object, data, cols, destination_key, nbins = 20,
  plot = TRUE, plot_stddev = TRUE)

Arguments

object	An H2OModel object.
data	An H2OFrame object used for scoring and constructing the plot.
cols	Feature(s) for which partial dependence will be calculated.
destination_key	An key reference to the created partial dependence tables in H2O.
nbins	Number of bins used. For categorical columns make sure the number of bins exceed the level count.
plot	A logical specifying whether to plot partial dependence table.
plot_stddev	A logical specifying whether to add std err to partial dependence plot.

Value

Plot and list of calculated mean response tables for each feature requested.

Examples

# NOT RUN {
library(h2o)
h2o.init()
prostate.path <- system.file("extdata", "prostate.csv", package="h2o")
prostate.hex <- h2o.uploadFile(path = prostate.path, destination_frame = "prostate.hex")
prostate.hex[, "CAPSULE"] <- as.factor(prostate.hex[, "CAPSULE"] )
prostate.hex[, "RACE"] <- as.factor(prostate.hex[,"RACE"] )
prostate.gbm <- h2o.gbm(x = c("AGE","RACE"),
                       y = "CAPSULE",
                       training_frame = prostate.hex,
                       ntrees = 10,
                       max_depth = 5,
                       learn_rate = 0.1)
h2o.partialPlot(object = prostate.gbm, data = prostate.hex, cols = c("AGE", "RACE"))
# }