Tmax

Curve

Scalar

Factor

Shape

Image

timeScale

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 data, shapes and images.  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 can either curves, scalars, factors, shapes or images. To this end, a new way of splitting the nodes of trees is introduced and the prediction procedures of trees and forests are generalized.

Louis Capitaine

FrechForest

Frechet Random Forests

Tmax function

Maximal Fr&lt;U+00E9&gt;chet tree — Tmax

Maximal Fr&lt;U+00E9&gt;chet tree

Tmax: Maximal Fr<U+00E9>chet tree

Description

Usage

Arguments