explain_tree: Explain a target using a simple decision tree (classification...

In explore: Simplifies Exploratory Data Analysis

Explain a target using a simple decision tree (classification or regression)

Description

Explain a target using a simple decision tree (classification or regression)

Usage

explain_tree(
  data,
  target,
  n,
  max_cat = 10,
  max_target_cat = 5,
  maxdepth = 3,
  minsplit = 20,
  cp = 0,
  weights = NA,
  size = 0.7,
  out = "plot",
  ...
)

Arguments

data	A dataset
target	Target variable
n	weights variable (for count data)
max_cat	Drop categorical variables with higher number of levels
max_target_cat	Maximum number of categories to be plotted for target (except NA)
maxdepth	Set the maximum depth of any node of the final tree, with the root node counted as depth 0. Values greater than 30 rpart will give nonsense results on 32-bit machines.
minsplit	the minimum number of observations that must exist in a node in order for a split to be attempted.
cp	complexity parameter. Any split that does not decrease the overall lack of fit by a factor of cp is not attempted. For instance, with anova splitting, this means that the overall R-squared must increase by cp at each step. The main role of this parameter is to save computing time by pruning off splits that are obviously not worthwhile. Essentially,the user informs the program that any split which does not improve the fit by cp will likely be pruned off by cross-validation, and that hence the program need not pursue it.
weights	optional case weights.
size	Text size of plot
out	Output of function: "plot" | "model"
...	Further arguments

Value

Plot or additional the model (if out = "model")

Examples

data <- iris
data$is_versicolor <- ifelse(iris$Species == "versicolor", 1, 0)
data$Species <- NULL
explain_tree(data, target = is_versicolor)

explore documentation built on Oct. 11, 2023, 9:07 a.m.