Gradient Boosting Explained: Turning Mistakes Into Precision

Author(s): Souradip Pal

Originally published on Towards AI.

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Imagine you’re a teacher, and you’re trying to predict the future salaries of your students based on two factors: their IQ and their CGPA (Cumulative Grade Point Average). Seems simple enough, right? But here’s the catch — you don’t want to make just any prediction; you want the most accurate prediction possible. So, instead of relying on one model to do all the work, you decide to use Gradient Boosting, an algorithm that cleverly combines the predictions of multiple models to get closer to the truth.

But how exactly does this magic happen? Let’s dive into the story of Gradient Boosting through a step-by-step journey using a sample dataset of five students. We’ll see how small, iterative improvements can lead to remarkably accurate predictions.

At its core, Gradient Boosting is a powerful machine learning technique used for regression and classification tasks. Unlike other algorithms, which rely on a single model to make predictions, Gradient Boosting uses a series of weak models (often decision trees), each learning from the mistakes of the one before it. The goal? To minimize the residual errors, or the difference between predicted and actual values, one step… Read the full blog for free on Medium.

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Published via Towards AI