XGBoost – What and how to use ? – Best Simple Guide

In this article, we’ll see in detail the advantages of the XGBoost library, how to use it, and why experts love it!

XGBoost is a library for training Gradient Boosting algorithms.

It allows to get the same kind of results as the sklearn library but… much faster!

Its efficiency and speed make it very popular among experts!

XGBoost is widely used by professionals in various fields, including Machine Learning, Data Science and Finance.

It is a powerful library for training Gradient Boosting algorithms.

Let’s dive into the details!

What is XGBoost ?

XGBoost stands for “eXtreme Gradient Boosting”.

It is a library allowing to train Gradient Boosting algorithms.

A type of algorithm that we already discussed in our article on ensemble methods.

To put it simply, ensemble methods allow you to use several Machine Learning algorithms at the same time!

Why would you do that?

Because it gives us a much more relevant result than if we only use one algorithm.

Gradient Boosting consists in training a series of models. Each model is trained in order to correct the errors of the previous one. The final prediction is made by combining the predictions of all the models in the series.

In the case of XGBoost, the trained models are Decision Trees.

The library is particularly useful because it can easily handle large datasets and complex models.

On top of that, it has many advanced features that will allow you to refine the training of your models.

How to use XGBoost in Python

To use XGBoost in Python, you must first install the library.

You can install it using the pip command:

!pip install xgboost

Once you have installed the library, import it into your Python code:

import xgboost as xgb

Ok now, we consider that you already have your X_train, y_train, X_test and y_test data.

To train an XGBoost model on these data, you need to convert them into a format that XGBoost can interpret: the xgb.DMatrix format.

To do this, simply use the DMatrix() function:

import xgboost as xgb

dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

We can then indicate the hyperparameters of our model :

param = {'objective': 'binary:logistic', 'eval_metric': 'error'}

And finally launch the training :

model = xgb.train(param, dtrain, num_boost_round=10)

You can find the exhaustive list of hyperparameters for XGBoost models on the official documentation.

To run a prediction, we simply use the predict() function on our test data:

model.predict(dtest)

And finally, we can calculate the accuracy of our model as follows:

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accuracy = sum(predictions == y_test) / len(y_test)
print('Accuracy: ', accuracy)

How does it look? Efficient ?

Give me your answer in comment 💥

How do experts use XGBoost?

Machine Learning experts often use XGBoost to train complex models for a variety of tasks, such as classification, regression and ranking.

They can use it alone or as part of a larger Machine Learning pipeline.

But one of the main advantages of XGBoost is that it is highly customizable.

Experts can modify a wide range of hyperparameters to refine the training process and improve model performance.

They can also use advanced features such as multi-threading and distributed training to further accelerate model training.

Experts also use XGBoost as part of an ensemble model.

The XGBoost algorithm is combined with predictions from other models to obtain a final prediction.

Since XGBoost is already an ensemble model, we have an ensemble of ensemble.

The Russian dolls of Machine Learning🪆

Obviously this often leads to improved performance.

Tips and tricks for using it

Here are some tips and tricks you can use to improve the performance of your XGBoost models:

  1. Hyperparameter Tuning: modify hyperparameters using grid search or random search.
  2. Early Stopping: use early stopping to avoid overfitting and find the optimal number of epochs.
  3. Learning Rate: try a higher learning rate, but keep in mind that this may increase the risk of overfitting.
  4. Number of estimators: use a larger number of estimators (Decision Tree), but be aware that this may increase the training time.
  5. More data: uses more data.

Previously, we wrote an article about the LightGBM library.

It is also a Gradient Boosting library.

You may wonder what is the difference between these two libraries…

Here is the answer 😉

Difference with LightGBM

XGBoost and LightGBM are two popular libraries for Gradient Boosting algorithm training.

They are both open-source and are used in a variety of Machine Learning tasks.

But there are some key differences between XGBoost and LightGBM:

  • Learning time: LightGBM is generally faster for training on large datasets. This is because LightGBM uses a more efficient algorithm for training trees. See the leaf-wise algorithm for more info.
  • Memory usage: LightGBM uses less memory.
  • Handling missing values: LightGBM can handle missing values in the dataset more efficiently than XGBoost by using histogram-based algorithms. See the Gradient-based One-Side Sampling (GOSS) technique for more information.
  • Handling categorical variable: LightGBM is able to handle categorical variables more efficiently than XGBoost, again using histogram-based algorithms. See the Exclusive Feature Bundling (EFB) technique for more info.

A good approach is to try both libraries and see which one works best for your particular problem.

We talk in detail about histogram based algorithms in our LightGBM overview article.

A must-read article for the more experienced among you or simply for the curious who want to use LightGBM!

See you soon on Inside Machine Learning! Ciao 🍕

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For the next 7 days I will show you how to use Neural Networks.

You will learn what Deep Learning is with concrete examples that will stick in your head.

BEWARE, this email series is not for everyone. If you are the kind of person who likes theoretical and academic courses, you can skip it.

But if you want to learn the PANE method to do Deep Learning, click here :

Tom Keldenich
Tom Keldenich

Data Engineer & passionate about Artificial Intelligence !

Founder of the website Inside Machine Learning

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