LightGBM: The Fastest Option for Gradient Magnification | by Gustavo R Santos | January, 2025
Learn how to implement a fast and efficient Gradient Boosting model using Python
When we talk Gradient Amplification Models [GBM]we often hear about Kaggle. This algorithm is very powerful, provides many tuning arguments, thus leads to very high accuracy metrics, and helps people to win competitions in that said field.
However, we are here to talk about real life. Or at least an implementation that we can apply to the problems that companies face.
Gradient Boosting is an algorithm that creates multiple models in sequence, always modeling on the error of the previous iteration and following the learning rate determined by the data scientist, until it reaches a plateau, no longer able to improve the test metric.
The Gradient Boosting algorithm creates successive models that try to minimize the error of the previous iteration.
The downside of GBMs is also what makes them so successful. Sequential construction.
If each new iteration is consecutive, the algorithm must wait for the completion of one iteration before starting another, increasing…
