plot_partials: Plot partial dependence plots

In jiho/joml: Utilities for Machine Learning

Plot partial dependence plots

Description

Plot partial dependence plots

Usage

plot_partials(
  object,
  fns = list(location = mean, spread = stats::sd),
  rug = TRUE
)

Arguments

object	an object output by ⁠[partials()]⁠, which contains a partial column.
fns	a list of summary functions; one should be called location and be used to compute the central location of the variable (e.g., mean, median, etc.); another should be called spread and be used to compute the spread around that location (e.g., sd, mad, etc.). When fns is NULL, the partial dependence is just concatenated across resamples.
rug	boolean; whether to add a rug plot to show at which values of the explanatory variables the partial dependence is computed. This is most useful when partial dependence is computed at quantiles of the original data (quantiles=TRUE in ⁠[partials()]⁠).

Value

A ggplot2 object.

See Also

Other partial dependence plots functions: partials(), summarise_partials()

Examples

# fit a model on 5 bootstraps
m <- resample_boot(mtcars, 5) %>%
  xgb_fit(resp="mpg", expl=c("cyl", "hp", "qsec"),
    eta=0.1, max_depth=4, nrounds=20)
# assess variable importance
importance(m) %>% summarise_importance()

# compute the partial dependence to the two most relevant variables
m <- partials(m, expl=c("hp", "cyl"))
# and plot them for each resample
plot_partials(m, fns=NULL)
# do the same with a finer grid
m <- partials(m, expl=c("hp", "cyl"), grid.resolution=50)
plot_partials(m, fns=NULL)
# or along quantiles
m <- partials(m, expl=c("hp", "cyl"), quantiles=TRUE, probs=0:20/20)
plot_partials(m, fns=NULL)

# compute mean+/-sd among resamples
summarise_partials(m)
plot_partials(m)
# do the same with median+/-mad
summarise_partials(m, fns=list(location=median, spread=mad))
plot_partials(m, fns=list(location=median, spread=mad))

jiho/joml documentation built on Dec. 6, 2023, 5:50 a.m.