Trains a Projection-Pursuit oblique decision tree.

This function trains a Projection-Pursuit oblique decision tree using either a formula and data frame interface or a matrix-based interface. When using the formula interface, specify the model formula and the data frame containing the variables. For the matrix-based interface, provide matrices for the features and labels directly. If lambda = 0, the model is trained using Linear Discriminant Analysis (LDA). If lambda > 0, the model is trained using Penalized Discriminant Analysis (PDA).

Usage

pptr(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  mode = NULL,
  lambda = 0.5,
  seed = NULL,
  pp = NULL,
  cutpoint = NULL,
  stop = NULL,
  binarize = NULL,
  grouping = NULL,
  leaf = NULL
)

Arguments

formula: A formula of the form y ~ x1 + x2 + ..., where y is a vector of labels and x1, x2, ... are the features.
data: A data frame containing the variables in the formula.
x: A matrix containing the features for each observation.
y: A matrix containing the labels for each observation.
mode: Training mode: either "classification" or "regression". When NULL (default), mode is auto-detected from y's type — factor or character vectors trigger classification, numeric vectors trigger regression. Setting it explicitly is useful for the binary-integer-labels case (mode = "classification" with integer 0/1 labels) and for failing fast on a type mismatch (mode = "regression" with a factor y errors immediately).
lambda: A regularization parameter (default: 0.5). If lambda = 0, the model is trained using Linear Discriminant Analysis (LDA). If lambda > 0, the model is trained using Penalized Discriminant Analysis (PDA). The default uses PDA because pure LDA (lambda = 0) is ill-conditioned when there are more variables than effective observations (see the "Known limitations" section of the README). Cannot be used together with pp.
seed: An optional integer seed for reproducibility. If NULL (default), a seed is drawn from R's RNG, so set.seed() controls reproducibility. If an integer is provided, that value is used directly.
pp: A projection pursuit strategy object created by pp_pda. Cannot be used together with lambda.
cutpoint: A split cutpoint strategy object created by cutpoint_mean_of_means (default).
stop: A stopping rule object. Default depends on mode: stop_pure_node() for classification, and stop_any(stop_min_size(5), stop_min_variance(0.01)) for regression.
binarize: A binarization strategy object. Default depends on mode: binarize_largest_gap() for classification, and binarize_disabled() for regression (regression's default grouping always yields a 2-group partition, so no binarization is needed).
grouping: A grouping strategy object. Default depends on mode: grouping_by_label() for classification, and grouping_by_cutpoint() for regression.
leaf: A leaf strategy object. Default depends on mode: leaf_majority_vote() for classification, and leaf_mean_response() for regression.

Value

A pptr model. Its S3 class vector is c("pptr_classification", "pptr", "ppmodel") or c("pptr_regression", "pptr", "ppmodel") depending on the mode.

Details

Mode is taken from the mode argument when explicit, and otherwise auto-detected from `y` (factor/character → classification, numeric → regression). Pass mode = "classification" to force classification on integer labels (e.g. binary 0/1), or mode = "regression" to assert intent on numeric responses.

Examples


# Example 1: formula interface with the `iris` dataset
pptr(Species ~ ., data = iris)
#> 
#> Call: pptr(formula = Species ~ ., data = iris)
#> 
#> Projection-Pursuit Oblique Decision Tree:
#> If ([ 0 -0.04 0.03 0.03 ] * x) < 0.01580044:
#>   Predict: setosa 
#> Else:
#>  If ([ 0 0.03 -0.06 -0.15 ] * x) < -0.4503323:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#> 

# Example 2: formula interface with the `iris` dataset with regularization
pptr(Species ~ ., data = iris, lambda = 0.5)
#> 
#> Call: pptr(formula = Species ~ ., data = iris, lambda = 0.5)
#> 
#> Projection-Pursuit Oblique Decision Tree:
#> If ([ 0 -0.04 0.03 0.03 ] * x) < 0.01580044:
#>   Predict: setosa 
#> Else:
#>  If ([ 0 0.03 -0.06 -0.15 ] * x) < -0.4503323:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#> 

# Example 3: matrix interface with the `iris` dataset
pptr(x = iris[, 1:4], y = iris[, 5])
#> 
#> Call: pptr(x = iris[, 1:4], y = iris[, 5])
#> 
#> Projection-Pursuit Oblique Decision Tree:
#> If ([ 0 -0.04 0.03 0.03 ] * x) < 0.01580044:
#>   Predict: setosa 
#> Else:
#>  If ([ 0 0.03 -0.06 -0.15 ] * x) < -0.4503323:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#>