predict.ODT {ODRF} | R Documentation |
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 |
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 |
... |
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
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)