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Explain a single prediction

Source: R/glex_explain.R
glex_explain.Rd

Plots the prediction components for a single observation, identified by the row number in the dataset used with glex(). Since the resulting plot can be quite busy due to potentially large amounts of elements, it is highly recommended to use predictors, max_interaction, or threshold to restrict the number of elements in the plot.

Usage

glex_explain(
  object,
  id,
  threshold = 0,
  max_interaction = NULL,
  predictors = NULL,
  class = NULL,
  barheight = 0.5
)

Arguments

object

Object of class glex containing prediction components and data to be explained.

id

(integer(1)) Row ID of the observation to be explained in object$x.

threshold

(numeric(1): 0) Threshold to filter output by in case of many negligible effects.

max_interaction

(integer(1): NULL) Optionally filter plot to show terms up to the specified degree of interaction. Similar to threshold, all other terms will be aggregated under a "Remaining terms" label.

predictors

(character: NULL) Vector of column names in $x to restrict plot to.

class

(character: NULL) For multiclass targets, specifies the target class to limit output.

barheight

(numeric(1): 0.5) Relative height of horizontal bars. Preferred value may depend on the number of vertical elements, hence it may be necessary to adjust this value as needed.

Value

A ggplot object.

See also

Other Visualization functions: autoplot.glex(), autoplot.glex_vi(), plot_pdp()

Examples

set.seed(1)
# Random Planted Forest -----
if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
library(randomPlantedForest)

rp <- rpf(mpg ~ ., data = mtcars[1:26, ], max_interaction = 2)

glex_rpf <- glex(rp, mtcars[27:32, ])

glex_explain(glex_rpf, id = 3, predictors = "hp", threshold = 0.01)
}