h2o (version 3.10.5.3)

h2o.partialPlot: Partial Dependence Plots

Description

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.

Usage

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

Arguments

object

An 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

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"))