Package 'svmodt'

Plot method for svmodt_node objects

Description

Thin S3 wrapper that dispatches to plot_boundary or plot_surface depending on plot.type.

Usage

## S3 method for class 'svmodt_node'
plot(
  x,
  y = NULL,
  ...,
  data = NULL,
  response = NULL,
  plot.type = c("surface", "boundary"),
  features = NULL,
  max_depth = NULL,
  check_accuracy = TRUE,
  resolution = NULL
)

Arguments

x	An svmodt_node returned by svm_split.
y	Ignored; present only to satisfy the graphics::plot generic signature.
...	Currently unused.
data	The original training data frame (required).
response	Character string naming the response column (required).
plot.type	One of "surface" (default) or "boundary".
features	Length-2 character vector of axis features ("surface" only; default uses root node features).
max_depth	Maximum depth to visualize ("boundary" only; default NULL = full tree).
check_accuracy	Logical; show per-node accuracy ("boundary" only; default TRUE).
resolution	Grid resolution per axis. Default 100 for "boundary", 200 for "surface".

Value

• "boundary": invisibly returns the list from plot_boundary.

• "surface": invisibly returns the ggplot2 object from plot_surface.

Examples

tree <- svm_split(wdbc, response = "diagnosis", max_depth = 3)

# All-node boundary panels - prints first, returns list
viz <- plot(tree,
  data = wdbc, response = "diagnosis",
  plot.type = "boundary"
)
viz$plots[[2]] # second node

# Global decision surface
plot(tree,
  data = wdbc, response = "diagnosis",
  plot.type = "surface"
)

# Surface with explicit feature axes
plot(tree,
  data = wdbc, response = "diagnosis",
  plot.type = "surface",
  features = c("radius_mean", "concavity_mean")
)

---

Predict method for svmodt_node objects

Description

Predict method for svmodt_node objects

Usage

## S3 method for class 'svmodt_node'
predict(object, newdata, return_probs = FALSE, calibrate_probs = TRUE, ...)

Arguments

object	An object of class svmodt_node.
newdata	A data frame of new predictor values.
return_probs	Logical; if TRUE, returns predictions and probabilities.
calibrate_probs	Logical; if TRUE, uses logistic calibration on decision values.
...	Currently unused.

Value

If return_probs = FALSE (the default), a character vector of predicted class labels, one element per row of newdata.

If return_probs = TRUE, a named list with two elements:

predictions

Character vector of predicted class labels (length = nrow(newdata)).

probabilities

Numeric matrix of class probabilities with nrow(newdata) rows and one column per class. Column names are the class labels; each row sums to 1. When calibrate_probs = TRUE, probabilities are derived from the SVM decision value via logistic calibration; otherwise empirical class frequencies at the leaf node are used.

Examples

# Train DTSVM tree
tree <- svm_split(
  data = wdbc,
  response = "diagnosis",
  max_depth = 3,
  max_features = 2,
  feature_method = "cor"
)

# Predict on WDBC data (returns a character vector of class labels)
preds <- predict(tree, newdata = wdbc)

# Predict with probabilities and logistic calibration
result <- predict(tree, newdata = wdbc,
  return_probs = TRUE, calibrate_probs = TRUE
)
head(result$predictions)
head(result$probabilities)

---

' Print method for svmodt_node objects

Description

' Print method for svmodt_node objects

Usage

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

Arguments

x	An object of class svmodt_node.
...	Further arguments passed to print_svm_tree.

Value

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.

Examples

tree <- svm_split(
  data = wdbc,
  response = "diagnosis",
  max_features = 2,
  max_depth = 3,
  min_samples = 5,
  feature_method = "random",
  verbose = TRUE
)
print(tree)

---

Build an Oblique Decision Tree Using SVM Splits

Description

Constructs a decision tree where each internal node uses a Support Vector Machine (SVM) to determine the split. Supports dynamic feature selection, feature penalization, scaling, and class weighting.

Usage

svm_split(
  data,
  response,
  depth = 1,
  max_depth = 10,
  min_samples = 5,
  max_features = NULL,
  feature_method = c("random", "mutual", "cor"),
  impurity_measure = c("entropy", "gini"),
  max_features_strategy = c("constant", "random", "decrease"),
  max_features_decrease_rate = 0.8,
  max_features_random_range = c(0.3, 1),
  penalize_used_features = FALSE,
  feature_penalty_weight = 0.5,
  n_subsets = 1,
  used_features = character(0),
  class_weights = c("none", "balanced", "custom"),
  custom_class_weights = NULL,
  min_impurity_decrease = 0.001,
  verbose = FALSE,
  all_classes = NULL,
  ...
)

Arguments

data	A data frame containing predictors and the response variable.
response	Character string specifying the response column in 'data'. All other columns are treated as predictors.
depth	Integer indicating the current recursion depth (used internally; default is 1).
max_depth	Maximum depth of the tree.
min_samples	Minimum number of samples required to attempt a split.
max_features	Maximum number of features to consider at each split.
feature_method	Feature selection method at each node. One of: '"random"': randomly select features, '"mutual"': select based on mutual information with the response, '"cor"': select based on correlation with the response.
impurity_measure	Information Gain evaluation criteria '"gini"': use Gini ratio '"entropy"': use Shannon entropy
max_features_strategy	Strategy to adjust the number of features per node: '"constant"': keep 'max_features' constant, '"decrease"': reduce features with depth, '"random"': randomly vary number of features within a range.
max_features_decrease_rate	Numeric fraction for decreasing features if 'max_features_strategy = "decrease"'.
max_features_random_range	Numeric vector of length 2 specifying min and max fraction of features if 'max_features_strategy = "random"'.
penalize_used_features	Logical; if TRUE, features used in ancestor nodes are penalized to encourage diversity.
feature_penalty_weight	Numeric (0<U+2013>1) weight for penalizing previously used features.
n_subsets	Number of Evaluated Random Feature combinations at each node when 'feature_method = "random'
used_features	Character vector of features already used in ancestor nodes (used internally).
class_weights	Character string specifying how to handle class imbalance. One of: '"none"': no weighting, '"balanced"': weight classes inversely proportional to their frequency, '"custom"': use 'custom_class_weights'.
custom_class_weights	Optional named numeric vector specifying custom weights per class.
min_impurity_decrease	Required decrease in impurity by a split to be considered valid
verbose	Logical; if TRUE, prints information about each node during tree construction.
all_classes	Optional character vector of all possible response classes (used internally).
...	Additional arguments passed to the underlying SVM fitting function.

Details

This function recursively splits the dataset using an SVM at each node. Splitting stops when maximum depth is reached, the node contains fewer than 'min_samples', or all samples belong to the same class. Features are scaled and selected dynamically at each node, and previously used features can be penalized to promote diversity. Class weighting schemes support handling imbalanced datasets. This approach allows construction of an **oblique decision tree**, where splits are linear hyperplanes rather than axis-aligned.

Value

A nested list representing the decision tree. Each node contains:

is_leaf

Logical; TRUE if the node is a leaf.

model

Fitted SVM model at this node (for internal nodes).

features

Vector of features selected for this node.

scaler

Scaling information used at this node.

left

Left child node (decision value > 0).

right

Right child node (decision value <U+2264> 0).

depth

Depth of this node in the tree.

n

Number of samples at this node.

max_features_used

Number of features considered at this node.

penalty_applied

Logical; TRUE if feature penalization was applied.

class_weights_used

Class weights applied at this node.

Examples

data(wdbc)
tree <- svm_split(
  data = wdbc,
  response = "diagnosis",
  max_depth = 3,
  min_samples = 5,
  feature_method = "random",
  verbose = TRUE
)

---

Trace the prediction path of a sample through an svmodt tree

Description

Generic function that walks the tree for a single row of new data, printing the SVM decision value and chosen branch at every internal node and the final predicted class at the leaf.

Usage

trace_path(object, ...)

## S3 method for class 'svmodt_node'
trace_path(object, sample_data, sample_idx = 1, ...)

Arguments

object	An svmodt_node returned by svm_split.
...	Currently unused.
sample_data	A data frame of new predictor values (one or more rows).
sample_idx	Integer; which row to trace (default 1).

Value

Invisibly returns the predicted class label (character string).

Methods (by class)

• trace_path(svmodt_node): Method for svmodt_node objects.

Examples

tree <- svm_split(wdbc, response = "diagnosis", max_depth = 3)
trace_path(tree, wdbc, sample_idx = 5)

---

Wisconsin Diagnostic Breast Cancer Dataset

Description

The WDBC dataset contains quantitative measurements from digitized images of fine needle aspirates (FNA) of breast masses. It is commonly used for classification tasks to distinguish between benign and malignant tumors.

Usage

wdbc

Format

A data frame with 569 rows and 32 columns:

radius_mean

Mean of radius

radius_se

Standard error of radius

radius_worst

Worst (largest) radius

texture_mean

Mean of texture

texture_se

Standard error of texture

texture_worst

Worst texture

perimeter_mean

Mean of perimeter

perimeter_se

Standard error of perimeter

perimeter_worst

Worst perimeter

area_mean

Mean area

area_se

Standard error of area

area_worst

Worst area

smoothness_mean

Mean smoothness

smoothness_se

Standard error of smoothness

smoothness_worst

Worst smoothness

compactness_mean

Mean compactness

compactness_se

Standard error of compactness

compactness_worst

Worst compactness

concavity_mean

Mean concavity

concavity_se

Standard error of concavity

concavity_worst

Worst concavity

concave.points_mean

Mean concave points

concave.points_se

Standard error of concave points

concave.points_worst

Worst concave points

symmetry_mean

Mean symmetry

symmetry_se

Standard error of symmetry

symmetry_worst

Worst symmetry

fractal_dimension_mean

Mean fractal dimension

fractal_dimension_se

Standard error of fractal dimension

fractal_dimension_worst

Worst fractal dimension

diagnosis

Factor with levels 'B' and 'M'

Source

Dr. William H. Wolberg, W. Nick Street, and Olvi L. Mangasarian, University of Wisconsin<U+2013>Madison. Original dataset available at: <https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic>

---

Wine Dataset

Description

The Wine dataset contains the results of a chemical analysis of wines derived from three different cultivars grown in the same region of Italy. The dataset is commonly used for multiclass classification tasks, where the objective is to identify the cultivar of origin based on physicochemical properties.

Usage

wine

Format

A data frame with 178 rows and 14 columns:

class

Factor with levels 1, 2, and 3 indicating cultivar

alcohol

Alcohol content

malic_acid

Malic acid concentration

ash

Ash content

alcalinity_of_ash

Alcalinity of ash

magnesium

Magnesium content

total_phenols

Total phenols

flavanoids

Flavonoid content

nonflavanoid_phenols

Nonflavanoid phenols

proanthocyanins

Proanthocyanin content

color_intensity

Color intensity

hue

Hue

od280_od315

OD280/OD315 of diluted wines

proline

Proline concentration

Source

Aeberhard, S. & Forina, M. (1992). Wine Dataset. UCI Machine Learning Repository. Original dataset available at: <https://archive.ics.uci.edu/dataset/109/wine>

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