OOB.tree

tree

[matrix]: matrix of the explanatory variables used to build the tree

[vector]: output curves used to build the tree

time

[numeric]: allow to modify the time scale, increasing or decreasing the cost of the horizontal shift. If timeScale is very big, then the Frechet mean tends to the Euclidean distance. If timeScale is very small, then it tends to the Dynamic Time Warping.

timeScale

Computes the oob error of a given tree:

internal

Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal heterogeneous data. In this package, we introduce Fr<c3><a9>chet trees and Fr<c3><a9>chet random forests, which allow to manage data for which input and output variables are curves. To this end, a new way of splitting the nodes of trees is introduced and the prediction procedures of trees and forests are generalized.

OOB.tree: OOB.tree

Description

Usage

Arguments