Understanding Gradient Boosting for Classification: A Practical Approach

Author(s): Souradip Pal

Originally published on Towards AI.

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Imagine you’re tasked with predicting whether students with varying CGPA and IQ levels get placed in a job or not. Now, you’ve got two options: go the simple way, using a single model, or bring out the heavy artillery — Gradient Boosting. Trust me, once you see how gradient boosting works its magic, the choice becomes obvious.

At its core, gradient boosting is a form of boosting — an ensemble technique that combines the predictions of several models (or weak learners) to improve overall accuracy. Instead of relying on one complex model to solve your problem, it stacks up a bunch of weak learners, gradually improving each one by focusing on the errors made by the previous learners.

The idea here is quite elegant: we start with a poor model and progressively refine it by focusing on the mistakes the previous model made. The more errors the model corrects, the better the ensemble becomes!

Under the hood of gradient boosting lies something called additive modeling. The name may sound complicated, but it’s simpler than you think. The idea behind additive modeling is to add up the predictions of multiple weak models to… Read the full blog for free on Medium.

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Published via Towards AI