pptr: Trains a Projection-Pursuit oblique decision tree.

In ppforest2: Projection Pursuit Oblique Decision Trees and Random Forests

Trains a Projection-Pursuit oblique decision tree.

Description

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.

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.

Value

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

See Also

predict.pptr_classification, predict.pptr_regression, formula.ppmodel, print.pptr, save_json, load_json, pp_tree for parsnip integration

Examples

# Example 1: formula interface with the `iris` dataset
pptr(Species ~ ., data = iris)

# Example 2: formula interface with the `iris` dataset with regularization
pptr(Species ~ ., data = iris, lambda = 0.5)

# Example 3: matrix interface with the `iris` dataset
pptr(x = iris[, 1:4], y = iris[, 5])

ppforest2 documentation built on July 21, 2026, 9:07 a.m.