Boosting is the process of building a large, additive decision tree by fitting a sequence of smaller decision trees, called layers. The tree at each layer consists of a small number of splits. The tree is fit based on the residuals of the previous layers, which allows each layer to correct the fit for bad fitting data from the previous layers. The final prediction for an observation is the sum of the predicted residuals for that observation over all the layers.