XGBoost - Over-fitting Control

XGBoost is capable to handle large data sets and build highly accurate models which makes it strong. Like any other machine learning model, XGBoost is vulnerable to over-fitting.

Because an over-fitted model collects too much information from the training set, which can contain noise and unimportant patterns, it can perform badly on new, unseen data. In this chapter, we will look at the management of over-fitting in XGBoost.

What is over-fitting?

Before we talk about how over-fitting happens in XGBoost and other gradient boosting models, let's first explain what over-fitting is. Over-fitting happens when a machine learning model pays too much attention to details that are specific to the training data. Instead of learning general patterns that work on other data the model focuses only on the special patterns in the training data. This makes it less useful when trying to make predictions on new data.

Why is over-fitting a problem?

Over-fitting is a problem because it limits the model's ability to function well with new data. If the model focuses too much on patterns that are specific to the training set, it will not be able to find patterns that work for additional data. This means the model will not give good results when used on new or different data.

This is an issue because the majority of machine learning models are designed specifically to identify broad patterns that can be applied to a wide range of people. When applied to unobserved data, a model that has over-fit to the training dataset will not be able to generate accurate predictions.

How to detect over-fitting with XGBoost

The good news is that over-fitting of a machine learning model can be easily identified. All you have to do is to determine that your machine learning model is over-fitting makes predictions on a dataset that was not encountered during training.

Your model is probably not over-fit to the training set if it performs well in making predictions on the unknown dataset. It is likely that your model has over-fit to the training data if the predictions it makes on the unknown data are much poorer than the predictions it makes on the training data.

Does XGBoost have an issue with over-fitting?

For the most part, XGBoost models will over-fit to the training set of data. This is particularly common when developing a complex model with multiple deep trees, or when training an XGBoost model on a limited training dataset.

Compared to other tree-based models like random forest models, XGBoost models have a higher tendency to over-fit to the dataset they were trained on. In general, random forest models are less sensitive to the selection of hyper-parameters used during training than XGBoost and gradient boosted tree models. This means that in order to evaluate the performance of models with various hyperparameter setups, it is very crucial to carry out hyperparameter optimization and make use of cross validation or a validation dataset.

How to avoid over-fitting with XGBoost

Here are some guidelines you can follow when creating an XGBoost or gradient boosted tree model to prevent over-fitting.

1. Use Fewer Trees

One technique to deal with over-fitting in your XGBoost model is to decrease the number of trees in your model. Large, multi-parameter models generally over-fit more frequently than simple, small models. You can simplify and reduce the probability of over-fitting your model by reducing the number of trees in the model.

2. Use Shallow Trees

One alternative way to simplify an XGBoost model and prevent it from over-fitting is to limit the model to use only shallow trees. Each tree therefore undergoes fewer splits, reducing the complexity of the model.

3. Use a Lower Learning Rate

Reducing the learning rate will also make your XGBoost model less vulnerable to over-fitting. This will serve as a regularization technique to prevent your model from being fixated on a pointless detail.

4. Reduce the Number of Features

Another excellent technique to simplify a machine learning model is to limit the features that it can use. This is another useful way to stop an XGboost model from over-fitting.

5. Use a Large Training Dataset

Your training dataset's size is an important factor that could affect how likely it is that your model will over-fit. Using a larger dataset will reduce the probability of over-fitting in your model. If you find that your XGBoost model is over-fitting and you have access to more training data, try increasing the quantity of data you are using to train your model.

Techniques to Control Over-fitting in XGBoost

In order to prevent over-fitting in XGBoost, we can use several approaches. Let's look at each one here −

• Regularization: Regularization is one way to keep the model from becoming excessively complex. The model finds it more difficult to store the data because complexity is penalized.

• Early Stopping: If, after a predefined number of cycles, the model's performance on a validation set does not improve, you may stop the training process using a technique called "early stopping". This prevents the model from training for an extended period of time and over-fitting the training set.

• Limiting the Depth of Trees: As mentioned before very deep trees capture too much detail, which can lead to over-fitting. The depth of the tree can be restricted to keep the model from being too complex.

• Learning Rate (Eta): The learning rate of the model determines how rapidly it learns. A greater learning rate leads to faster learning but the capacity of the model to suddenly change its non-universally distributed learning patterns can result to over-fitting.