Convert C5.0 model to party object

Convert a single tree from a C5.0 decision tree or boosted model to a party object for use with partykit visualization and analysis tools.

Usage

# S3 method for class 'C5.0'
as.party(obj, tree = 1L, data = NULL, ...)

Arguments

obj

A C5.0 object from the C50 package.

tree

Integer specifying which tree to convert (1-based indexing, default is 1). For single tree models, use tree = 1. For boosted models with trials > 1, this selects which boosting iteration to extract.

data

Data.frame containing the training data, including both predictors and response variable. Required for proper party object creation with fitted values and node summaries.

...

Not currently used.

Value

A party object from the partykit package.

Details

C5.0 tree storage format

The C50 package stores trees in a custom text format in obj$tree. This format uses indented lines with key-value pairs:

• type="2": Internal node with split

• type="0": Terminal/leaf node

• att="VariableName": Attribute/variable to split on

• forks="n": Number of branches (2+ for numeric, can be 4+ for categorical)

• cut="threshold": Numeric threshold for split

• class="ClassName": Predicted class

• freq="n1,n2,n3": Frequency of each class at node

Boosting and trials

• Single tree models (trials = 1): Only tree = 1 is valid

• Boosted models (trials > 1): Multiple sequential trees available

• The tree parameter maps to trial/iteration number

• Each boosting trial produces one tree

Tree structure

• Trees stored as sequential lines in pre-order (parent, then children)

• No indentation used - hierarchy determined by fork counts

• Numeric ternary splits: <= threshold, missing, > threshold

• Categorical multiway splits: one branch per level group

Split encoding

• Numeric splits: typically binary (<=, >) or ternary (<=, missing, >)

• Ternary numeric splits are simplified to binary by omitting the missing branch

• Categorical splits: can have 2+ branches, one for each level group

• Multiway categorical splits are preserved in the party object

Variable names

• obj$predictors contains ordered list of predictor variable names

• att attribute in tree text references these by name

• Map to 1-based indices for partykit

Important limitations

• C50's text format is complex and may vary by version

• Ternary numeric splits simplified to binary (missing value branch omitted)

• Rule-based models (obj$rules != "") not supported

• Some terminal nodes may have n=0 (empty branches where no observations fall)

Examples

if (rlang::is_installed(c("C50", "palmerpenguins"))) {
  data(penguins, package = "palmerpenguins")
  penguins <- na.omit(penguins)

  # Single tree model
  set.seed(2847)
  c5_tree <- C50::C5.0(species ~ ., data = penguins)
  party_tree <- as.party(c5_tree, tree = 1L, data = penguins)
  print(party_tree)
  plot(party_tree)

  # Boosted model with multiple trials
  set.seed(5193)
  c5_boost <- C50::C5.0(species ~ ., data = penguins, trials = 3)
  # Extract first boosting iteration
  party_tree1 <- as.party(c5_boost, tree = 1L, data = penguins)
  # Extract third boosting iteration
  party_tree3 <- as.party(c5_boost, tree = 3L, data = penguins)
}
#> 
#> Model formula:
#> species ~ island + bill_length_mm + bill_depth_mm + flipper_length_mm + 
#>     body_mass_g + sex + year
#> 
#> Fitted party:
#> [1] root
#> |   [2] flipper_length_mm < 206: Adelie (n = 208, err = 30.8%)
#> |   [3] flipper_length_mm >= 206
#> |   |   [4] island in Biscoe: Gentoo (n = 118, err = 0.0%)
#> |   |   [5] island in Dream, Torgersen
#> |   |   |   [6] bill_length_mm < 46.5: Adelie (n = 2, err = 0.0%)
#> |   |   |   [7] bill_length_mm >= 46.5: Chinstrap (n = 5, err = 0.0%)
#> 
#> Number of inner nodes:    3
#> Number of terminal nodes: 4