### Overview

Gradient boosting is a technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically prediction trees. It builds the model in a stage-wise fashion like other boosting methods do, and generalizes them by allowing optimization of an arbitrary differentiable loss function.

### Motivation

Consider a least-squares regression setting, where the goal is to "teach" a model to predict values in the form by minimizing the mean squared error , averaged over some training set of actualy values of the output variable .

At each stage , of gradient boosting, it may be assumed that there is a some imperfect model . The gradient boosing algorithm improves on by constructing a new model that adds an estimator to provide a better model: .

To find , the gradient boosting solution starts with the observation that a perfect would imply

i.e. that adding the extra predictor will make up for all the mistakes our previous model did, or equivalently

Therefore, gradient boosting will fit to the residual . Like in other boosting variants, each learns to correct its predecessor .

The generalization to other loss-functions than the least-squares, comes from the observation that for a given model are the negative gradients (wrt. ) of the squared error loss function, hence for any loss function we'll simply fit to the negative gradient .

### Algorithm

Input: training set , a differentiable loss function , number of iterations .

1. Initialize model with a constant value:

2. For to :

1. Compute so-called psuedo-residuals :
1. Fit a base learner (e.g. tree) to psuedo-residuals, i.e. train it using the training set
2. Compute multiplier sby solving the following one-dimension optimization problem
1. Update the model:
3. Output

## Gradient Tree Boosting

• Uses decision trees of fixed size as base learners

### Modified gradient boosting method ("TreeBoost")

• Generic gradient boosting at the m-th step would fit a decision tree to psuedo-residuals
• Let be the number of its leaves
• Tree partitions input space into disjoint regions and predicts a constant value in each region, i.e.

where is the value predicted in the region .

In the case of usual CART trees, where the trees are fitted using least-squares loss, and so the coefficient for the region is equal to just the value of the output variable, averaged over all training instances in .

The modified gradient tree boosting ("TreeBoost") algorithm is then to instead choose a separate optimal value for each of the tree's regions, instead of a single for whole tree:

where we've simply absorbed into .

### Terminology

• Out-of-bag error / estimate is a method for measuring prediction error of methods utilizing bootstrap aggregating (bagging) to sub-sample data used for training. OOB is the mean prediction error on each training sample , using only the trees that did not have in their bootstrap sample

### Regularization

• Number of trees: Large number of trees can overfit
• Shrinkage: use a modified update rule:

where is the learnin rate.

• Subsampling is when at each iteration of the algorithm, a base learner should be fit on a subsample of the training set drawn at random without replacement. Sumsample size is the fraction of training set to use; if then algorithm is deterministic and identitical to TreeBoost
• Limit number of observations in trees' terminal nodes: in the tree building process by ignoring any split that lead to nodes containing fewer than this number of training set instances. Helps reduce variance in predictions at leaves.
• Penalize complexity of tree: model complexity can be defined as proportional number of leaves in the learned trees. Joint optimization of loss and model complexity corresponds to a post-pruning algorithm.
• Regularization (e.g. , ) on leaf nodes