Understanding the Math Behind Gradient Boosting: A Step-by-Step Guide

Author(s): Souradip Pal

Originally published on Towards AI.

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Imagine you’re trying to predict housing prices based on various factors, such as location, size, or the number of bedrooms. The challenge? The data you’re working with is complex, non-linear, and noisy. Machine learning is your go-to solution, but what if even a simple regression can’t quite crack the code? Enter gradient boosting, a clever method that uses a series of weak models (often decision trees) to form a strong prediction engine. In this blog, we’ll unravel the math behind gradient boosting and how it works to capture these complex relationships.

At its core, machine learning (ML) is all about teaching machines to learn from data. It’s a process where algorithms find patterns and relationships between input (features) and output (target) data. For example, if you’re predicting housing prices, the inputs could be square footage, location, and number of bedrooms, and the output would be the predicted price.

ML models work by mapping this input to output through some mathematical function. But, depending on how complex the data is, this can be quite tricky.

Let’s start simple. If the relationship between your inputs and output is linear, we can capture it using… Read the full blog for free on Medium.

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Published via Towards AI