predict.ODT: making predict based on ODT objects

In ODRF: Oblique Decision Random Forest for Classification and Regression

making predict based on ODT objects

Description

Prediction of ODT for an input matrix or data frame.

Usage

## S3 method for class 'ODT'
predict(object, Xnew, leafnode = FALSE, ...)

Arguments

object	An object of class ODT, the same as that created by the function ODT.
Xnew	An n by d numeric matrix (preferable) or data frame. The rows correspond to observations and columns correspond to features. Note that if there are NA values in the data 'Xnew', which will be replaced with the average value.
leafnode	If or not output the leaf node sequence number that Xnew is partitioned. (default FALSE)
...	Arguments to be passed to methods.

Value

A vector of the following:

• prediction: the prediced response of the new data.

• leafnode: the leaf node sequence number that the new data is partitioned.

References

Zhan, H., Liu, Y., & Xia, Y. (2022). Consistency of The Oblique Decision Tree and Its Random Forest. arXiv preprint arXiv:2211.12653.

See Also

ODT predict.ODRF

Examples

# Classification with Oblique Decision Tree.
data(seeds)
set.seed(221212)
train <- sample(1:209, 100)
train_data <- data.frame(seeds[train, ])
test_data <- data.frame(seeds[-train, ])

tree <- ODT(varieties_of_wheat ~ ., train_data, split = "entropy")
pred <- predict(tree, test_data[, -8])
# classification error
(mean(pred != test_data[, 8]))

# Regression with Oblique Decision Tree.
data(body_fat)
set.seed(221212)
train <- sample(1:252, 100)
train_data <- data.frame(body_fat[train, ])
test_data <- data.frame(body_fat[-train, ])

tree <- ODT(Density ~ ., train_data, split = "mse")
pred <- predict(tree, test_data[, -1])
# estimation error
mean((pred - test_data[, 1])^2)

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