Introduction to Boosted Trees -- xgboost 0.6 documentation

Based on different understandings of \( y_i \) we can have different problems, such as regression, classification, ordering, etc. We need to find a way to find the best parameters given the training data. In order to do so, we need to define a so-called objective function, to measure the performance of the model given a certain set of parameters. A very important fact about objective functions is they must always contain two parts: training loss and regularization. The training loss measures how predictive our model is on training data.