Update existing ODT using new data to improve the model.
Usage
# S3 method for ODT
online(obj, X = NULL, y = NULL, weights = NULL, MaxDepth = Inf, ...)Arguments
- obj
an object of class
ODT.- X
An new n by d numeric matrix (preferable) or data frame used to update the object of class
ODT.- y
A new response vector of length n used to update the object of class
ODT.- weights
Vector of non-negative observational weights; fractional weights are allowed (default NULL).
- MaxDepth
The maximum depth of the tree (default
Inf).- ...
optional parameters to be passed to the low level function.
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, ])
index <- seq(floor(nrow(train_data) / 2))
tree <- ODT(varieties_of_wheat ~ ., train_data[index, ], split = "gini")
online_tree <- online(tree, train_data[-index, -8], train_data[-index, 8])
pred <- predict(online_tree, test_data[, -8])
# classification error
(mean(pred != test_data[, 8]))
#> [1] 0.1009174
# 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, ])
index <- seq(floor(nrow(train_data) / 2))
tree <- ODT(Density ~ ., train_data[index, ], split = "mse")
online_tree <- online(tree, train_data[-index, -1], train_data[-index, 1])
pred <- predict(online_tree, test_data[, -1])
# estimation error
mean((pred - test_data[, 1])^2)
#> [1] 6.186264e-05