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XGBoost Explained: A Beginner’s Guide

Understand how XGBoost works, when to use it, and its advantages over other algorithms

Photo by Ali Shah Lakhani on Unsplash.

If you are interested in Machine Learning, you have probably heard of XGBoost before, and are wondering what XGBoost is, how it works, and when to use it. In this article, I’ll make XGBoost relatively simple and guide you through the data science process, showcasing its strengths and advantages over other algorithms, including Large Language Models (LLMs).

What is XGBoost?

XGBoost, or Extreme Gradient Boosting, represents a cutting-edge approach to machine learning that has garnered widespread acclaim for its exceptional performance in tackling classification and regression problems. By leveraging an ensemble of decision trees, XGBoost constructs a powerful predictive model through an iterative process that focuses on minimising errors. This iterative refinement, driven by gradient descent optimisation (the ability of the model to find the answer with the least error quickly), enables XGBoost to continually enhance its predictive accuracy by strategically updating the parameters of the decision trees. Moreover, XGBoost incorporates regularisation techniques, or more simply, techniques to combat overfitting, ensuring the model’s ability to generalise well to unseen data. The combination of ensemble learning, gradient descent optimisation, and regularisation makes XGBoost a formidable tool for data scientists and practitioners seeking highly accurate and efficient solutions to complex predictive modelling challenges.

In practice, XGBoost has emerged as a go-to algorithm across a multitude of domains, including finance, healthcare, and e-commerce, due to its versatility and effectiveness. Its ability to handle large datasets and complex feature interactions makes it particularly well-suited for real-world applications where predictive accuracy is paramount. Furthermore, XGBoost’s interpretability enhances its appeal by providing valuable insights into the underlying patterns driving predictions.

How does XGBoost work?

At its core, XGBoost employs a technique called gradient boosting. This is where the algorithm starts by creating a simple decision tree and then iteratively adds more trees to the model, each one focusing on correcting the errors made by the previous trees. This process continues until a predetermined number of trees is reached or the model’s performance no longer improves significantly.

XGBoost introduces several key enhancements to the standard gradient boosting algorithm:

1. Regularisation: XGBoost incorporates regularisation techniques, such as L1 (Lasso Regression) and L2 (Ridge Regression) regularisation to prevent overfitting and improve model generalisation to help prevent models from getting too complex and overfitting the data. L1 regularisation simplifies the model by removing less important features, while L2 regularisation keeps the model stable by balancing the influence of different features.
2. Tree Pruning: XGBoost employs a technique called “tree pruning” to limit the depth of decision trees, avoiding overly complex and potentially overfit models.
3. Parallel Processing: XGBoost supports parallel processing, enabling faster training times on large datasets by utilising multiple CPU cores.
4. Handling Missing Values: XGBoost can automatically handle missing values in the dataset, reducing the need for extensive data preprocessing.

When to use XGBoost?

XGBoost excels in various scenarios and has proven to be a top choice for many data scientists. Here are some situations where XGBoost shines:

1. Tabular Data: XGBoost is particularly well-suited for structured, tabular data, such as data stored in CSV files or SQL databases.
2. Large Datasets: XGBoost’s efficiency and parallel processing capabilities make it an excellent choice for handling large datasets with numerous features.
3. Feature Importance: XGBoost provides built-in feature importance scores, allowing you to identify the most influential variables in your model.
4. Imbalanced Data: XGBoost offers hyperparameters, such as `scale_pos_weight`, which can be used to address class imbalance in the data, making it effective for imbalanced classification problems.

Customer Churn Prediction: A Classic XGBoost Use Case

Imagine you’re working for a subscription-based online streaming service. A critical business challenge is understanding why customers cancel their subscriptions, also known as “churn.”

XGBoost is ideally suited for this task.

The first step in tackling this problem is data preparation. You might have data on customer demographics, viewing habits (genres, hours spent watching, etc.), subscription plans, and more. XGBoost can handle various data types, including numbers and categories, making it well-equipped to process this diverse dataset.

Next, you would use XGBoost to train a predictive model on this data. The model learns to identify complex patterns that correlate with customers who are likely to churn. XGBoost’s ability to capture intricate relationships and interactions among features makes it a powerful tool for this task.

Once the model is trained, XGBoost’s feature importance scores could reveal the most influential factors driving churn. Perhaps surprisingly, the most expensive subscription plan has a high churn rate, or customers who primarily watch a specific genre are more likely to leave. These insights provide valuable information about the factors contributing to customer churn.

Armed with this knowledge, the streaming company can tailor retention strategies. Maybe they offer targeted discounts for those on the expensive plan or create more content in the popular-yet-underserved genre. XGBoost’s insights enable the company to make data-driven decisions and take proactive measures to reduce churn.

The key takeaway is that XGBoost doesn’t just predict churn, it helps you understand the reasons behind it. By unravelling the complex patterns and relationships in the data, XGBoost empowers businesses to make informed decisions and implement targeted interventions to retain customers and improve their bottom line.

See an example Python script at Bloch-AI/XGBoost_Demo: Supporting notebook for the Medium Article XGBoost Explained: A Beginners Guide (github.com)

For a low-code approach, you can opt for the XGBoost nodes in KNIME.

Why is XGBoost so popular?

Well, its simple and effective. As a result, XGBoost has gained immense popularity and has consistently outperformed other machine learning algorithms in various competitions and real-world applications. Its success can be attributed to several factors:

1. Robustness: XGBoost is known for its ability to handle a wide range of datasets and problems, delivering high accuracy and performance across different domains.
2. Flexibility: XGBoost provides a wide array of hyper-parameters that can be tuned to optimise model performance, allowing for detailed control over the learning process.
3. Scalability: XGBoost’s parallel processing capabilities enable it to handle large-scale datasets efficiently, making it suitable for big data applications.
4. Interpretability: XGBoost’s feature importance scores provide insights into the model’s decision-making process, enhancing interpretability and aiding in feature selection.
5. Proven Track Record: XGBoost has consistently performed exceptionally well in numerous machine learning competitions, such as Kaggle, where it has historically been a key component in many winning solutions. This has contributed to its widespread adoption and popularity among data scientists and machine learning practitioners.

XGBoost vs. Large Language Models (LLMs)

While Large Language Models (LLMs) like GPT-4 are impressive for tasks like generating text and analysing sentiments, XGBoost is practically unbeatable for handling structured data. LLMs shine in understanding and producing human-like text, thanks to their extensive training. However, when dealing with structured data with clear features, XGBoost’s tailored design and efficiency make it the go-to choice. Its ability to work with tabular data, along with its straightforward approach and reliability in capturing complex relationships, often makes it the preferred tool for predictive modelling tasks. While LLMs have their place in language-related tasks, XGBoost remains a solid choice for structured data analysis, providing accurate and reliable classification results with minimal effort.

XGBoost Alternatives: XGBoost vs CatBoost vs LightGBM

CatBoost and LightGBM are gradient boosting algorithms that share similarities with XGBoost in their ability to handle supervised learning tasks. CatBoost excels in handling categorical features without the need for preprocessing, making it user-friendly and robust to overfitting. It offers efficient training speed and memory usage, but may have limited flexibility in hyperparameter tuning compared to XGBoost. LightGBM, on the other hand, utilises a histogram-based algorithm for unparalleled speed and efficiency, particularly with large datasets and high-dimensional features. It supports parallel and GPU training, delivering fast performance, but may be prone to overfitting on smaller datasets.

When considering which algorithm to choose, XGBoost is a versatile option with extensive hyperparameter tuning capabilities, strong community support, and robustness in handling missing data and sparse inputs. CatBoost is ideal for datasets with categorical features and when computational resources are limited, while LightGBM shines in scenarios requiring fast training speeds and efficient memory usage, especially on large-scale datasets. Ultimately, the choice depends on the specific characteristics of the dataset, available computational resources, and the specific requirements of the task at hand.

The optimal algorithm to use often varies based on both the dataset nuances and your performance requirements. Don’t hesitate to experiment with all three; a less theoretically ‘perfect’ model might surprise you in practice. Also, remember that XGBoost, CatBoost, and LightGBM are constantly evolving. Updates and improvements may reshape their performance characteristics over time, making it worthwhile to revisit your algorithm choices periodically.

Conclusion

XGBoost is a powerful model for building highly accurate and efficient predictive models. By understanding how XGBoost works, when to use it, and its advantages over other algorithms, beginners can unlock its potential and apply it effectively in their data science projects. Whether you’re working with tabular data, dealing with imbalanced datasets, or seeking interpretable models, XGBoost should be a core part of your machine learning toolkit.

Jamie is a Managing Director at Grant Thornton UK LLP, and a Visiting Fellow in Enterprise AI at Manchester Metropolitan University. He prefers cheese toasties.

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