If you know Voltron — you know Gradient Boosting. A team of quirky robotic lions teaming up to fight evil is the basic idea (just swap “evil” for “messy data”). Gradient boosting is a trick in machine learning where we build a strong model by combining a bunch of not-so-great ones. Imagine you’re putting together a team of average Joes who, on their own, are just okay — but when they join forces, they’re the Avengers of predictive power, fixing each other’s mistakes like overachieving roommates.

Each lion here represents a weak model, and their combined form represents the strong final model. Each weak model learns from the mistakes of the previous ones to contribute to the overall fight against “messy data”.

That is a short version. A long one is as follows:

It’s called Gradient Boosting because of how the algorithm builds (boosts) new models based on the gradient of the loss function.

1. Boosting:

• Boosting means combining many simple, weak models (usually small decision trees), each correcting errors from previous models, into a single, powerful model.
• Every subsequent model attempts to boost performance by focusing on mistakes of previous models.

2. Gradient (direction of improvement):

• The gradient in mathematics is a measure of how a function changes — an arrow pointing toward the direction of the steepest improvement.
• In Gradient Boosting, this refers specifically to the gradient of the loss function — the function that measures how wrong predictions are of the previous model/step.

Initially, you have a model with some error (loss). Each new model focuses specifically on predicting the negative gradient (direction of steepest descent) of the loss, effectively indicating how predictions should change to reduce error. By repeatedly moving in the direction of steepest descent, the algorithm gradually improves, getting closer to the optimal solution.

Imagine we have three houses with their locations and prices. We’ll use tables to make it clear:

Initial Dataset

Press enter or click to view image in full size

Step 1: Calculate the Mean Price

First, we find the average price: (100 + 200 + 150) / 3 = 150. So, initially, we predict 150 for each house.

Step 2: Calculate Residuals

Next, we find the errors (residuals) by subtracting the predicted price from the actual price:

• House 1: 100–150 = -50
• House 2: 200–150 = 50
• House 3: 150–150 = 0

Press enter or click to view image in full size

These residuals represent the negative gradient the next weak learner will try to predict.

Step 3: Build a Tree to Predict Residuals

We use a decision tree based on location. For Location A (Houses 1 and 3), the average residual is -25; for Location B (House 2), it’s 50.

Step 4: Update Predictions with Learning Rate

Let’s assume a learning rate of 0.1, and adjust predictions. Learning rate scales down the contribution of each new weak learner to prevent overfitting and promote gradual improvement.

• House 1: 150 + 0.1 * (-25) = 147.5
• House 2: 150 + 0.1 * 50 = 155
• House 3: 150 + 0.1 * (-25) = 147.5

Press enter or click to view image in full size

Step 5: New Residuals

We calculate new errors:

• House 1: 100–147.5 = -47.5
• House 2: 200–155 = 45
• House 3: 150–147.5 = 2.5

Press enter or click to view image in full size

We’d repeat this adding more correcting layers on top, but for simplicity, we stop here. An unexpected detail is that Gradient Boosting can handle missing data without extra preprocessing, which is handy for real-world messy datasets.

Gradient Boosting vs. Neural Networks and other techniques

Gradient Boosting excels at structured/tabular data, requires less data to achieve high performance, simpler tuning whereas Neural Networks best suited for large-scale, unstructured data like images or text.

Gradient Boosting is often chosen over other machine learning techniques for several reasons:

1. High Accuracy

Gradient Boosting consistently achieves superior accuracy, frequently outperforming other algorithms like logistic regression, random forests, or neural networks in structured/tabular datasets.

2. Works Well with Tabular Data — unlike deep learning, which typically excels at unstructured data (like images or text), Gradient Boosting is particularly suited to structured/tabular data (numerical and categorical features).

3. Built-in Feature Selection and Importance — Gradient Boosting algorithms naturally provide methods to evaluate feature importance, helping to understand what variables drive the prediction.

4. Handles Complex Relationships — it effectively captures nonlinear relationships and interactions between variables without explicit manual feature engineering.

5. Robustness and Stability — Gradient Boosting methods, especially modern implementations like XGBoost, LightGBM, or CatBoost, are highly robust and less prone to overfitting due to built-in regularization techniques.

6. Effective on Various Problem Types — it performs well on regression, classification, and ranking tasks, making it versatile across industries and applications.

7. Good Default Performance — Gradient Boosting often provides strong results out-of-the-box, requiring less fine-tuning compared to neural networks.

Modern Gradient Boosting Libraries

• XGBoost (Extreme Gradient Boosting), developed by Tianqi Chen. It optimizes GBM implementation, providing better speed, accuracy, and scalability. Becomes widely popular in Kaggle competitions.
• LightGBM by Microsoft Research. It provides faster training on large-scale data, using techniques like histogram-based algorithms and leaf-wise tree growth.
• CatBoost, developed by Yandex, specifically focusing on improved handling of categorical data.

Put your questions in the comment section.

That’s it, have fun!