---
title: "Contrast and Boosted Trees"
author: "Jerome Friedman"
date: '`r Sys.Date()`'
output:
  html_document:
  fig_caption: yes
  theme: cerulean
  toc: yes
  toc_depth: 2
vignette: >
  %\VignetteIndexEntry{conTree}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

Contrast trees are used to assess the accuracy of many types of
machine learning estimates that are not amenable to standard
validation techniques. These include properties of the conditional
distribution $p_{y}(y\,|\,\mathbf{x})$ (means, quantiles, complete
distribution) as functions of $\mathbf{x}$. Given a set of predictor
variables $\mathbf{x}=(x_{1},x_{2},$$,x_{p})$ and two outcome
variables $y$ and $z$ associated with each $\mathbf{x}$, a contrast
tree attempts to partition the space of $\mathbf{x}$ values into local
regions within which the respective distributions of
$y\,|\,\mathbf{x}$ and $z\,|\,\mathbf{x}$, or selected properties of
those distributions such as means or quantiles, are most different.

## A Tutorial

1. [Introduction](https://jhfhub.github.io/conTree_tutorial/)
2. [Examples](https://jhfhub.github.io/conTree_tutorial/examples.html)
3. [Package Function Reference](https://jhfhub.github.io/conTree_tutorial/contree.html)