# Around gradient boosting: classification, missing values, second order derivatives, and line search.

This post is a sort of follow-up to this introduction to gradient boosting as a gradient descent.

It’s a collection of notes about how gradient boosting works in practice, and how to implement a GBDT estimator. We will cover:

• how to handle binary and multiclass classification tasks
• how to support missing values
• the use of second-order derivatives for appropriate losses
• how to implement some tricky losses like least absolute deviation

These sections are independent.

## Binary classification

In our previous post, we described gradient boosting for regression. In fact, training a GBDT for classification is exactly the same. The only thing that changes is the loss function.

It is quite analoguous to the linear regression / logistic regression thing.

In order to support the binary cross-entropy loss (or log loss, or logistic loss, or negative log-likelihood), we only need to adapt the compute_gradient_of_loss() function: instead of returning the gradient of the least squares loss, we just return the gradient of the cross-entropy loss.

With this loss, the trees do not predict a probability, they predict a log-odds ratio (just like in logistic regression). To get the probability that the sample belongs to the positive class, we just need to apply the logistic sigmoid function to the raw values coming from the trees. There’s an implementation in this notebook.

Note that, even for classification tasks, the trees that we build are regression trees! They are not classification trees. Indeed, we need the trees to predict the gradients, and the gradients are continuous.

## Multiclass classification

Adding support for multiclass classification involves a few changes to the base algorithm. The main one is that instead of building 1 tree per iteration (like in binary classification and regression), we build K trees per iteration, where K is the number of classes.

Each tree is a kind of OVR tree, but trees are not completely independent because they influence each other when we compute the gradients (and the hessians).

Concretely, the K trees of the ith iteration do not depend on each other, but each tree at iteration i depends on all the K trees of iteration i - 1 (and before).

For a given sample, the probability that it belongs to class k is computed as a regular softmax between raw_predictions = [raw_predictions_0, raw_predictions_1, ... raw_predictions_K-1],

where raw_prediction_k is the sum of the raw predictions coming from all the trees of the kth class. The predicted class is then the argmax of the K probabilities.

## Support for missing values

Decision trees (and thus GBDTs), have an elegant native support for missing values.

It’s deceptively simple, but this section requires a working knowledge of how decision trees are trained.

When considering a potential split point (identified by a feature index and a threshold value), some samples are mapped to the left child, and the rest are mapped to the right child. This mapping is based on whether the feature value of the samples at the node is lower (or greater) than the given threshold.

If there are missing values, when computing the gain at a potential split point, we additionally consider these 2 hypothetical scenarios:

• samples with missing values go to the left child?
• samples with missing values go to the right child?

We compute the gain of both these alternatives, and just keep the best one.

In practice, considering these two scenarios is performed very simply by scanning the potential thresholds from left to right (ascending order), and then from right to left (descending order). There’s a neat explaination in Alg. 3 of the XGBoost paper.

When predicting, the samples with missing values for a given split point go to the best alternative.

Another great feature of decision trees is that missing values can still be supported at predict time, even if no missing values were encountered at fit time. A common strategy is to map nodes with missing values to whichever child has the most samples.

## Second order derivatives

Some losses, like the log-loss, have non-constant second order derivatives (abusively called hessians). In that case, instead of performing a gradient descent step, it is more efficient to perform a Newton-Raphson step: concretely, instead of predicting the gradients, the trees try to predict the ratio gradients / hessians.

This corresponds to equation (5) of the XGBoost paper. (This isn’t really new, and was already mentionned in the original paper by Jerome Friedman).

## Line search for Least Absolute Deviation loss

The least absolute deviation loss is defined as $\mathcal{L} = \sum_i |y_i - \hat{y}_i|$.

Unlike other losses, this loss requires changing the values predicted by the trees after they are trained.

We still build the trees by fitting the gradients (just like with any other loss), but once a tree is trained, we update its predicted values with the median of the samples in each leave.

Remember how gradient boosting is analoguous to gradient descent? Well updating the tree values (again, only after the tree is trained) corresponds to the line search procedure of gradient descent.

In gradient descent, the line search consists in computing an optimal value for the learning rate. In gradient boosting, this translates in updating the tree values.

If you don’t replace the values, you’ll have terrible predictions. This is not surprising, since with this loss the gradients can only take the values -1 or 1 (and you can’t correctly approximate a continuous function with a sum of integers).

In fact, this line search is not specific to the LAD loss. We do apply this line search for all losses, including least squares and log loss. The thing is, for these losses, the line search dictates that the values of the trees should be exactly what they were trained with, so we just don’t need to update them. With LAD, the line search dictates that the values should be updated with a median.

I encourage you to refer to the original paper by Jerome Friedman for a more theoretically grounded analysis. You can also find more details about line search for gradient descent in Boyd and Vandenberghe’s book.