' Print method for svmodt_node objects
# S3 method for class 'svmodt_node'
print(x, ...)An object of class svmodt_node.
Further arguments passed to print_svm_tree.
Invisibly returns x (the svmodt_node object), called
for its side effect of printing a human-readable summary of the tree
structure to the console.
# \donttest{
tree <- svm_split(
data = wdbc,
response = "diagnosis",
max_features = 2,
max_depth = 3,
min_samples = 5,
feature_method = "random",
verbose = TRUE
)
#>
#> --- Node at depth 1 ---
#> Samples: 569
#> Class distribution:
#>
#> B M
#> 357 212
#> Number of classes at node: 2
#> Selected features: perimeter_worst, symmetry_se
#> Features after removing constants: perimeter_worst, symmetry_se
#> Binary classification case
#> Classes at node : B, M
#> Class counts : 357, 212
#> Class weights: none (equal weights)
#>
#> --- Node at depth 2 ---
#> Samples: 196
#> Class distribution:
#>
#> B M
#> 16 180
#> Number of classes at node: 2
#> Selected features: compactness_worst, texture_worst
#> Features after removing constants: compactness_worst, texture_worst
#> Binary classification case
#> Classes at node : B, M
#> Class counts : 16, 180
#> Class weights: none (equal weights)
#>
#> --- Node at depth 2 ---
#> Samples: 373
#> Class distribution:
#>
#> B M
#> 341 32
#> Number of classes at node: 2
#> Selected features: fractal_dimension_mean, smoothness_worst
#> Features after removing constants: fractal_dimension_mean, smoothness_worst
#> Binary classification case
#> Classes at node : B, M
#> Class counts : 341, 32
#> Class weights: none (equal weights)
print(tree)
#> [Node] depth = 1 | n = 569 | features = [perimeter_worst,symmetry_se] | penalty = +
#> |- Positive branch (distance >= 0):
#> | [Leaf] predict = M | n = 196 | features = [compactness_worst,texture_worst]
#> `- Negative branch (distance < 0):
#> [Leaf] predict = B | n = 373 | features = [fractal_dimension_mean,smoothness_worst]
# }