Trains a Random Forest of Project-Pursuit oblique decision trees.

This function trains a Random Forest of Project-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. The number of trees is controlled by the size parameter. Each tree is trained on a stratified bootstrap sample drawn from the data. The number of variables to consider at each split is controlled by the n_vars parameter. 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

pprf(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  size = 2,
  lambda = 0,
  n_vars = NULL,
  p_vars = NULL,
  seed = NULL,
  max_retries = 3L,
  threads = NULL,
  pp = NULL,
  dr = NULL,
  sr = 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.
size: The number of trees in the forest.
lambda: A regularization parameter. If lambda = 0, the model is trained using Linear Discriminant Analysis (LDA). If lambda > 0, the model is trained using Penalized Discriminant Analysis (PDA). Cannot be used together with pp.
n_vars: The number of variables to consider at each split (integer). These are chosen uniformly in each split. The default is all variables. Cannot be used together with p_vars or dr.
p_vars: The proportion of variables to consider at each split (number between 0 and 1, exclusive). For example, p_vars = 0.5 uses half the features. Cannot be used together with n_vars or dr.
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. The same seed is used for training and for computing permuted variable importance.
max_retries: Maximum number of retries for degenerate trees (default: 3). When a bootstrap sample yields a singular covariance matrix, the tree is retrained with a different seed up to this many times.
threads: The number of threads to use. The default is the number of cores available.
pp: A projection pursuit strategy object created by pp_pda. Cannot be used together with lambda.
dr: A dimensionality reduction strategy object created by dr_uniform or dr_noop. Cannot be used together with n_vars or p_vars.
sr: A split rule strategy object created by sr_mean_of_means (default).

Value

A pprf model trained on x and y.

Examples


# Example 1: formula interface with the `iris` dataset
pprf(Type ~ ., data = iris)
#> 
#> Random Forest of Project-Pursuit Oblique Decision Tree
#> -------------------------------------
#> Tree 1:
#> If ([ 0.01 0.05 -0.03 -0.02 ] * x) < 0.06262772:
#>  If ([ 0.04 0.09 -0.1 -0.13 ] * x) < -0.2146854:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#> Else:
#>   Predict: setosa 
#> 
#> Tree 2:
#> If ([ 0 0.05 -0.04 -0.01 ] * x) < 0.06345474:
#>  If ([ 0.06 0.05 -0.1 -0.12 ] * x) < -0.1598488:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#> Else:
#>   Predict: setosa 
#> 
#> 

# Example 2: formula interface with the `iris` dataset with regularization
pprf(Type ~ ., data = iris, lambda = 0.5)
#> 
#> Random Forest of Project-Pursuit Oblique Decision Tree
#> -------------------------------------
#> Tree 1:
#> If ([ 0 -0.04 0.02 0.03 ] * x) < 0.02115907:
#>   Predict: setosa 
#> Else:
#>  If ([ 0 0.04 -0.06 -0.16 ] * x) < -0.4436958:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#> 
#> Tree 2:
#> If ([ 0 -0.03 0.03 0.04 ] * x) < 0.03752498:
#>   Predict: setosa 
#> Else:
#>  If ([ 0 0.02 -0.06 -0.16 ] * x) < -0.5112084:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#> 
#> 

# Example 3: matrix interface with the `iris` dataset
pprf(x = iris[, 1:4], y = iris[, 5])
#> 
#> Random Forest of Project-Pursuit Oblique Decision Tree
#> -------------------------------------
#> Tree 1:
#> If ([ 0.02 0.04 -0.04 -0.02 ] * x) < 0.08615907:
#>  If ([ 0.1 0 -0.12 -0.12 ] * x) < -0.1784111:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#> Else:
#>   Predict: setosa 
#> 
#> Tree 2:
#> If ([ 0.02 0.03 -0.05 0 ] * x) < 0.03522417:
#>  If ([ 0.05 0.04 -0.09 -0.16 ] * x) < -0.2597719:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#> Else:
#>   Predict: setosa 
#> 
#> 

# Example 4: matrix interface with the `iris` dataset with regularization
pprf(x = iris[, 1:4], y = iris[, 5], lambda = 0.5)
#> 
#> Random Forest of Project-Pursuit Oblique Decision Tree
#> -------------------------------------
#> Tree 1:
#> If ([ 0 -0.05 0.02 0.04 ] * x) < -0.04241288:
#>   Predict: setosa 
#> Else:
#>  If ([ 0 0.04 -0.05 -0.16 ] * x) < -0.3842216:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#> 
#> Tree 2:
#> If ([ 0 -0.04 0.02 0.04 ] * x) < 0.009132806:
#>   Predict: setosa 
#> Else:
#>  If ([ 0 0.03 -0.06 -0.14 ] * x) < -0.4294196:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#> 
#> 

# Example 5: formula interface with the `crabs` dataset
pprf(Type ~ ., data = crabs)
#> 
#> Random Forest of Project-Pursuit Oblique Decision Tree
#> -------------------------------------
#> Tree 1:
#> If ([ 0.13 0 0 0 0 0 0 ] * x) < 0.06243492:
#>   Predict: O 
#> Else:
#>   Predict: B 
#> 
#> Tree 2:
#> If ([ 0.14 0 0 0 0 0 0 ] * x) < 0.0746413:
#>   Predict: O 
#> Else:
#>   Predict: B 
#> 
#> 

# Example 6: formula interface with the `crabs` dataset with regularization
pprf(Type ~ ., data = crabs, lambda = 0.5)
#> 
#> Random Forest of Project-Pursuit Oblique Decision Tree
#> -------------------------------------
#> Tree 1:
#> If ([ 0.01 -0.01 0.01 0.01 0 0 0.01 ] * x) < 0.3329027:
#>   Predict: B 
#> Else:
#>   Predict: O 
#> 
#> Tree 2:
#> If ([ 0.01 -0.01 -0.01 0 0 0 -0.01 ] * x) < -0.3170045:
#>   Predict: O 
#> Else:
#>   Predict: B 
#> 
#>

Trains a Random Forest of Project-Pursuit oblique decision trees.

Usage

Arguments

Value

See also

Examples