Random Planted Forest

Usage

rpf(x, ...)

# S3 method for class 'data.frame'
rpf(
  x,
  y,
  max_interaction = 1,
  ntrees = 50,
  splits = 30,
  split_try = 10,
  t_try = 0.4,
  split_decay_rate = 0.1,
  max_candidates = 50,
  delete_leaves = TRUE,
  deterministic = FALSE,
  nthreads = 1,
  purify = FALSE,
  cv = FALSE,
  loss = "L2",
  delta = 0,
  epsilon = 0.1,
  split_structure = "leaves",
  export_forest = FALSE,
  ...
)

# S3 method for class 'matrix'
rpf(
  x,
  y,
  max_interaction = 1,
  ntrees = 50,
  splits = 30,
  split_try = 10,
  t_try = 0.4,
  split_decay_rate = 0.1,
  max_candidates = 50,
  delete_leaves = TRUE,
  deterministic = FALSE,
  nthreads = 1,
  purify = FALSE,
  cv = FALSE,
  loss = "L2",
  delta = 0,
  epsilon = 0.1,
  split_structure = "leaves",
  export_forest = FALSE,
  ...
)

# S3 method for class 'formula'
rpf(
  formula,
  data,
  max_interaction = 1,
  ntrees = 50,
  splits = 30,
  split_try = 10,
  t_try = 0.4,
  split_decay_rate = 0.1,
  max_candidates = 50,
  delete_leaves = TRUE,
  deterministic = FALSE,
  nthreads = 1,
  purify = FALSE,
  cv = FALSE,
  loss = "L2",
  delta = 0,
  epsilon = 0.1,
  split_structure = "leaves",
  export_forest = FALSE,
  ...
)

# S3 method for class 'recipe'
rpf(
  x,
  data,
  max_interaction = 1,
  ntrees = 50,
  splits = 30,
  split_try = 10,
  t_try = 0.4,
  split_decay_rate = 0.1,
  max_candidates = 50,
  delete_leaves = TRUE,
  deterministic = FALSE,
  nthreads = 1,
  purify = FALSE,
  cv = FALSE,
  loss = "L2",
  delta = 0,
  epsilon = 0.1,
  split_structure = "leaves",
  export_forest = FALSE,
  ...
)

Arguments

x, data: Feature matrix, or data.frame, or recipe.
...: (Unused).
y: Target vector for use with x. The class of y (either numeric or factor) determines if regression or classification will be performed.
max_interaction: [1]: Maximum level of interaction determining maximum number of split dimensions for a tree. The default 1 corresponds to main effects only. If 0, the number fo columns in x is used, i.e. for 10 predictors, this is equivalent to setting max_interaction = 10.
ntrees: [50]: Number of trees generated per family.
splits: [30]: Number of splits performed for each tree family.
split_try: [10]: Number of split points to be considered when choosing a split candidate.
t_try: [0.4]: A value in (0,1] specifying the proportion of viable split-candidates in each round.
split_decay_rate: [0.1]: Exponential decay factor for aging split-candidates. Possible splits are initiated with age=0. Whenever a possible split becomes a split_candidate (i.e. it has been drawn when drawing max(max_candidates , t_try * possible options ) times) it ages by +1. The age of the single split-candidate with minimal loss is reset to zero. Split_candidates are sampled from Possible_splits with weight exp(-split_decay_rate_ * age). A high split_decay_rate means faster aging. split_decay_rate=0 results in no aging and uniform sampling.
max_candidates: [50]: Maximum number of split-candidates sampled per iteration. Number of split_candidates in each round is given by max(max_candidates , t_try * possible options).
delete_leaves: [TRUE]: Whether to delete a parent leaf when splitting along an existing dimension.
deterministic: [FALSE]: Choose whether approach deterministic or random.
nthreads: [1L]: Number of threads used for computation, defaulting to serial execution.
purify: [FALSE]: Whether the forest should be purified. Set to TRUE to enable components extract with predict_components() are valid. Can be achieved after fitting with purify().
cv: [FALSE]: Determines if cross validation is performed.
loss: ["L2"]: For regression, only "L2" is supported. For classification, "L1", "logit" and "exponential" are also available. "exponential" yields similar results as "logit" while being significantly faster.
delta: [0]: Only used if loss is "logit" or "exponential". Proportion of class membership is truncated to be smaller 1-delta when calculating the loss to determine the optimal split.
epsilon: [0.1]: Only used if loss = "logit" or "exponential". Proportion of class membership is truncated to be smaller 1-epsilon when calculating the fit in a leaf.
split_structure: ["leaves"]: Defines the structure of a possible split and how to choose split_candidates. Can be one of "leaves", "hist", "cur_trees_1", "cur_trees_2", or "res_trees". Further details are given below.
export_forest: [FALSE]: Whether to store the flattened forest in the returned object as $forest. If FALSE, $forest is NULL, reducing memory use of the returned object.
formula: Formula specification, e.g. y ~ x1 + x2.

Value

Object of class "rpf" with model object contained in $fit.

Details

splits

The number of splits is the main tuning parameter affecting the accuracy of predictions.

split_structure

The split_structure argument controls how split candidates are constructed and sampled. In each round, a t_try fraction (capped by max_candidates) is drawn from the pool of all possible splits with weights exp(-split_decay_rate * age).

leaves: Split candidates are (leaf, split-dimension) pairs. For each sampled candidate, split_try thresholds are drawn uniformly from the valid range within that leaf and evaluated to choose the best split.
cur_trees_1: Split candidates are (current-tree, split-dimension) pairs. For each sampled candidate, perform split_try evaluations. Each evaluation samples a leaf from the set of valid current trees (with probability proportional to its number of available thresholds) and then uniformly samples a single threshold within that leaf.
cur_trees_2: Split candidates are (current-tree, split-dimension) pairs. For each sampled candidate, iterate through every valid leaf. Within each leaf, sample split_try thresholds uniformly and evaluate them.
res_trees: Split candidates are resulting trees. For each sampled candidate, run split_try evaluations by sampling a (split-dimension, leaf) pair from all valid pairs (with probability proportional to its number of available thresholds), then uniformly sampling one threshold within that pair.

Examples

# Regression with x and y
rpfit <- rpf(x = mtcars[, c("cyl", "wt")], y = mtcars$mpg)

# Regression with formula
rpfit <- rpf(mpg ~ cyl + wt, data = mtcars)