prune.ODRF: Pruning of class 'ODRF'.

In ODRF: Oblique Decision Random Forest for Classification and Regression

Pruning of class ODRF.

Description

Prune ODRF from bottom to top with test data based on prediction error.

Usage

## S3 method for class 'ODRF'
prune(obj, X, y, MaxDepth = 1, useOOB = TRUE, ...)

Arguments

obj	An object of class ODRF.
X	An n by d numeric matrix (preferable) or data frame is used to prune the object of class ODRF.
y	A response vector of length n.
MaxDepth	The maximum depth of the tree after pruning (Default 1).
useOOB	Whether to use OOB for pruning (Default TRUE). Note that when useOOB=TRUE, X and y must be the training data in ODRF.
...	Optional parameters to be passed to the low level function.

Value

An object of class ODRF and prune.ODRF.

• ppForest The same result as ODRF.

• pruneError Error of test data or OOB after each pruning in each tree, misclassification rate (MR) for classification or mean square error (MSE) for regression.

See Also

ODRF online.ODRF prune.ODT

Examples

# Classification with Oblique Decision Random Forest
data(seeds)
set.seed(221212)
train <- sample(1:209, 80)
train_data <- data.frame(seeds[train, ])
test_data <- data.frame(seeds[-train, ])
forest <- ODRF(varieties_of_wheat ~ ., train_data,
  split = "entropy", parallel = FALSE, ntrees = 50
)
prune_forest <- prune(forest, train_data[, -8], train_data[, 8])
pred <- predict(prune_forest, test_data[, -8])
# classification error
(mean(pred != test_data[, 8]))

# Regression with Oblique Decision Random Forest
data(body_fat)
set.seed(221212)
train <- sample(1:252,80)
train_data <- data.frame(body_fat[train, ])
test_data <- data.frame(body_fat[-train, ])
index <- seq(floor(nrow(train_data) / 2))
forest <- ODRF(Density ~ ., train_data[index, ], split = "mse", parallel = FALSE, ntrees = 50)
prune_forest <- prune(forest, train_data[-index, -1], train_data[-index, 1], useOOB = FALSE)
pred <- predict(prune_forest, test_data[, -1])
# estimation error
mean((pred - test_data[, 1])^2)

ODRF documentation built on May 31, 2023, 8:22 p.m.