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Gradient Boosting And XGBoost

Shirin Glander has another English-language transcript from a German video, this time covering gradient boosting techniques:

Let’s look at how Gradient Boosting works. Most of the magic is described in the name: “Gradient” plus “Boosting”.

Boosting builds models from individual so called “weak learners” in an iterative way. In the Random Forests part, I had already discussed the differences between Bagging and Boostingas tree ensemble methods. In boosting, the individual models are not built on completely random subsets of data and features but sequentially by putting more weight on instances with wrong predictions and high errors. The general idea behind this is that instances, which are hard to predict correctly (“difficult” cases) will be focused on during learning, so that the model learns from past mistakes. When we train each ensemble on a subset of the training set, we also call this Stochastic Gradient Boosting, which can help improve generalizability of our model.

The gradient is used to minimize a loss function, similar to how Neural Nets utilize gradient descent to optimize (“learn”) weights. In each round of training, the weak learner is built and its predictions are compared to the correct outcome that we expect. The distance between prediction and truth represents the error rate of our model. These errors can now be used to calculate the gradient. The gradient is nothing fancy, it is basically the partial derivative of our loss function – so it describes the steepness of our error function. The gradient can be used to find the direction in which to change the model parameters in order to (maximally) reduce the error in the next round of training by “descending the gradient”.

Along with neural networks, gradient boosting has become one of the dominant algorithms for machine learning, and is well worth learning about.