Unraveling Gradient Boosting Machines (GBM) for Regression Model: A Step-by-Step Guide

4 min readSep 5, 2024

Introduction:

Hello, Everyone! 🌟 In the world of machine learning, Gradient Boosting Machines (GBMs) have gained widespread popularity due to their flexibility, accuracy, and ability to handle various types of data. If you’ve heard of GBM but aren’t quite sure what it is or how it works, you’re in the right place! In this blog, we’ll break down GBM step-by-step, explaining how it uses the Gradient Boosting technique to create powerful predictive models.

Note: The blog assumes that readers have knowledge of how Gradient Boosting and Regression Tree works. For a quick refresher, you can go through my blogs about Gradient descent here and Regression Tree in this link.

Let’s dive in!

What is GBM?

Gradient Boosting Machine (GBM) is a machine learning algorithm that builds a series of weak learners, typically decision trees, and combines them to form a strong predictive model. It’s a boosting algorithm, meaning it builds models sequentially, with each new model correcting the errors made by the previous ones. GBMs are used for both classification and regression problems, offering great flexibility in handling structured data.

How Does GBM Work?

GBM is based on the principle of “boosting.” Boosting refers to combining the predictions of several weak learners (simple models) to form a strong learner. In the case of GBM, these weak learners are decision trees. Here’s how GBM works step by step:

Step-by-Step Explanation of GBM Regression Model

1. Initialize the model with a constant value (F₀(x)):

Start by predicting a constant value for all samples. The initial prediction is often the mean of the target values.

2. For each iteration (or each tree):

Step 1: Compute the residuals (errors) for each sample by computing the gradient of the loss function(mean square loss function here) w.r.t the prediction:

Computing error for each sample

Where yi is the actual target value, and Fm(xi) is the prediction from the current model at iteration m.
Step 2: Train a new decision tree to fit the residuals: This tree learns how to reduce the remaining error in the prediction by approximating the residuals.
Step 3: Make predictions with the new tree hm(x): The newly trained tree predicts corrections to the original model’s predictions.
Step 4: Update the model by adding the predictions from the new tree, scaled by the learning rate:

3. Repeat the process for a predefined number of iterations (or until a stopping criterion is met, like minimizing the loss function).

4. Final Prediction: Once the model has completed all iterations, the final prediction is the sum of all trees’ predictions.

Gradient Boosting: How Does the “Gradient” Part Work?

The key idea behind GBM is to use gradient descent to minimize the loss function (a measure of error). In each iteration, the algorithm fits a new model to the negative gradient of the loss function with respect to the current predictions. This means that each weak learner is trying to point the overall model in the direction that most reduces the error.

Here’s how gradient boosting works:

Loss Function: Choose a loss function that measures the error. For regression, the most common loss function is the Mean Squared Error (MSE). For classification, it’s typically the log-loss function.
Gradient Descent: In each iteration, the algorithm calculates the gradient of the loss function with respect to the model’s predictions. This gradient represents the direction in which the model should be adjusted to reduce the error.
Update Model: The weak learner (a small decision tree) is fitted to the negative gradient, helping the model to make corrections in the right direction. The learning rate is used to scale the size of the correction made by each learner.

Why Use GBM? (Advantages)

Accuracy: GBM tends to be more accurate than many other algorithms, especially on structured data.
Flexibility: It can handle both classification and regression problems.
Feature Importance: GBM provides insight into feature importance, allowing you to understand which features contribute most to the predictions.
Handles Missing Data: GBM can handle missing data effectively.

Challenges of GBM (Disadvantages)

Overfitting: Since GBM is a very powerful algorithm, it can overfit the training data if not properly tuned (e.g., too many trees, high learning rate).
Training Time: GBM can be computationally expensive, especially when a large number of iterations are needed.
Parameter Tuning: GBM has many hyperparameters (e.g., learning rate, number of trees, tree depth) that need careful tuning to achieve optimal performance.

Conclusion

Gradient Boosting Machines (GBMs) are one of the most powerful algorithms in machine learning, capable of making highly accurate predictions. By understanding the step-by-step process and how it uses the Gradient Boosting technique, you can better appreciate the workings of GBM and how it can be applied to various problems. Although GBM is complex and requires tuning, its power and flexibility make it a go-to choice for many data scientists.

Happy boosting! 🚀