gpboost (version 1.6.4)

gpb.plot.partial.dependence: Plot partial dependence plots

Description

Plot partial dependence plots

Usage

gpb.plot.partial.dependence(model, data, variable, latent_scale = TRUE,
  n.pt = 100, subsample = pmin(1, n.pt * 100/nrow(data)),
  discrete.x = FALSE, which.class = NULL,
  xlab = deparse(substitute(variable)), ylab = "", type = if (discrete.x)
  "p" else "b", main = "", return_plot_data = FALSE, ...)

Value

A two-dimensional matrix with data for creating the partial dependence plot. This is only returned if return_plot_data==TRUE

Arguments

model

A gpb.Booster model object

data

A matrix with data for creating partial dependence plots

variable

A string with a name of the column or an integer with an index of the column in data for which a dependence plot is created

latent_scale

If TRUE, the plot is done on the scale of the tree-ensemble. If FALSE, the plot is done on the (potentially transformed) response variable scale (e.g. probabilities for classification)

n.pt

Evaluation grid size (used only if x is not discrete)

subsample

Fraction of random samples in data to be used for calculating the partial dependence plot

discrete.x

A boolean. If TRUE, the evaluation grid is set to the unique values of x

which.class

An integer indicating the class in multi-class classification (value from 0 to num_class - 1)

xlab

Parameter passed to plot

ylab

Parameter passed to plot

type

Parameter passed to plot

main

Parameter passed to plot

return_plot_data

A boolean. If TRUE, the data for creating the partial dependence plot is returned

...

Additional parameters passed to plot

Author

Fabio Sigrist (adapted from a version by Michael Mayer)

Examples

# \donttest{
library(gpboost)
data(GPBoost_data, package = "gpboost")

gp_model <- GPModel(group_data = group_data[,1], likelihood = "gaussian")
gpboost_model <- gpboost(data = X,
                         label = y,
                         gp_model = gp_model,
                         nrounds = 16,
                         learning_rate = 0.05,
                         max_depth = 6,
                         min_data_in_leaf = 5,
                         verbose = 0)
gpb.plot.partial.dependence(gpboost_model, X, variable = 1)
# }