Gradient Boosting - Learning Guide
Feb 22, 2025
Estimated reading time - 6 minutes
Intro
Gradient Boosting (GB) is arguably the second most important algorithm in Machine Learning and Data Science after Neural Networks of different kinds. And the most important one for tabular data.
From my experience, proper understanding of how GB works makes a big difference in making most of it in real world projects. I have seen people blindly fitting GB to tabular data without realizing mistakes they make.
Below is the roadmap for learning GB under the hood and proper examples of how to tune its hyperparameters.
Roadmap overview
Here is the GB learning roadmap overview:
Let's break straight into it.
Step 1: Deep Overview
There are so many introductions to Gradient Boosting and 99% either miss the point to explain the technical details of the algorithms or just scratch the surface.
A good start to grasp the idea behind how Gradient Boosting works is the Gradient Boosting topic of the Open Machine Learning Course.
This tutorial is a well-structured intro, balancing theory with practical implementation. It covers key concepts like weak learners, boosting principles, and model tuning, making it a great starting point for data scientists looking to understand and apply Gradient Boosting effectively.
Step 2: Hyperparameters deep dive
To understand each hyperparameter better, Gradient boosting machines, a tutorial (part 1-4) is the way to go.
It provides an in-depth explanation of not only the hyperparameters like learning rate and tree depth but also explains different loss function families and weak learners.
Step 3: From scratch implementation
From scratch implementation is the best way to understand how the theory is implemented in practice and fully grasp the algo idea.
You don't need to implement this yourself but just going through the code (maybe even with a debugger) line by line will help you to understand what the algorithm actually does.
This is a good implementation example to go through:
Step 4: Hyperparameters visualization
The next great step to solidify understanding of how hyperparameters influence the fitting process is to see it visually.
For that, these 2 demos do a great job:
Step 5: Hyperparameters optimization tutorial
Of course, nobody implements GB from scratch in real projects. Now, when you have a deep algo and its hyperparameters understanding, it's time to learn how to properly tune them in practice.
This tutorial provides detailed examples of how to tune GB hyperparameters using Random Search and Bayesian Optimization (using Hyperopt) approaches. In addition, it provides detailed visualization of the tuning results and comparison between the approaches.
Step 6: CatBoost, XGBoost and LightGBM
There are three most widely used implementations of the GB - CatBoost, XGBoost and LightGBM.
Each of them has its own strengths and weaknesses that come from the implementation details.
Understanding the difference between them will help you to:
- choose the right one for your problem
- answer interview questions regarding these differences
- understand why (or why not) a particular method works for your problem
These two resources is good place to start:
Step 7: Application of GB for forecasting
In most tutorials, GB is applied to classification or pure regression problems. So, this part you'll get covered with resources above.
However, GB is also a powerful algo for forecasting and in many real-world multivariate forecasting systems, GB is widely used. Forecasting is a big part of ML business in many industries, so I believe knowing how to use GB for that will be a great tool to have in your arsenal.
This tutorial will provide you with a great overview of how GB can be applied for forecasting problems.
Conclusion
This blog article provided you with step-by-step resources to master Gradient Boosting - one of the most powerful ML algorithms. You will never regret learning from these resources both for your current role and interviews for future positions.
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