Print and summarise a CHAID tree

print() displays the tree structure with one line per node showing the prediction, node size and split information. summary() additionally reports the control settings and a risk estimate on the training data (misclassification rate or expected misclassification cost for categorical responses, weighted within-node variance for continuous responses).

Usage

# S3 method for class 'chaid'
print(x, ...)

# S3 method for class 'chaid'
summary(object, ...)

Arguments

x, object

A fitted "chaid" object returned by chaid().

...

Ignored.

Value

The fitted object, invisibly.

See also

chaid()

Examples

fit <- chaid(Species ~ ., data = iris,
             control = chaid_control(min_parent = 30, min_child = 10))
print(fit)
#> CHAID decision tree (method = "chaid")
#> Response: Species (categorical) 
#> Valid cases: 150 (data: 150 rows) 
#> 
#> [1] root: setosa (33.3%), n=150 | split: Petal.Length (adj.p=1.35e-44, chi2=243.8, B=126)
#>   [2] Petal.Length in {<= 1.6}: setosa (100.0%), n=44 *
#>   [3] Petal.Length in {(1.6, 3.8]}: versicolor (57.1%), n=14 *
#>   [4] Petal.Length in {(3.8, 4.6]}: versicolor (96.9%), n=32 *
#>   [5] Petal.Length in {(4.6, 4.9]}: versicolor (64.3%), n=14 *
#>   [6] Petal.Length in {(4.9, 5.3]}: virginica (87.5%), n=16 *
#>   [7] Petal.Length in {> 5.3}: virginica (100.0%), n=30 *
summary(fit)
#> CHAID decision tree (method = "chaid")
#> Response: Species (categorical) 
#> Valid cases: 150 (data: 150 rows) 
#> 
#> [1] root: setosa (33.3%), n=150 | split: Petal.Length (adj.p=1.35e-44, chi2=243.8, B=126)
#>   [2] Petal.Length in {<= 1.6}: setosa (100.0%), n=44 *
#>   [3] Petal.Length in {(1.6, 3.8]}: versicolor (57.1%), n=14 *
#>   [4] Petal.Length in {(3.8, 4.6]}: versicolor (96.9%), n=32 *
#>   [5] Petal.Length in {(4.6, 4.9]}: versicolor (64.3%), n=14 *
#>   [6] Petal.Length in {(4.9, 5.3]}: virginica (87.5%), n=16 *
#>   [7] Petal.Length in {> 5.3}: virginica (100.0%), n=30 *
#> 
#> Settings: alpha_merge=0.05, alpha_split=0.05, bonferroni=TRUE, adjust_across=none, max_depth=3, min_parent=30, min_child=10, n_bins=10
#> 
#> Risk estimate (training data): 0.0933 (misclassification rate) 
#> 
#> Terminal nodes: 6 
#> Stopping reasons:
#>   min_child: 1
#>   min_parent: 3
#>   pure: 2