XGBoost: Its Present-day Powers and Use Cases for Machine Learning

Author(s): Anil Tilbe

Originally published on Towards AI.

Being that XGBoost achieves implementations with the ability to handle missing values, which are one of the major drawbacks in most of the other algorithms, scalabilities, not just time-efficiencies, are very promising for the adoption of XGBoost.

By Possessed Photography from Unsplash

In the simplest terms, XGBoost is a sequence tree for ranking.

1. It automatically learns the model of the input (usually, the feature) and then fits a new one (this is where the approach gained the name gradient boosting.)

2. It is an ensemble learning algorithm that uses a gradient descent framework as the base for the construction of the gradient descent estimator. Gradient Boosting is associated with gradient descent methods and one of the best-performing ensemble methods because of its ability to yield accurate results.

4. It arrives with many performances tuning hyper-parameters, encapsulating the components of an… Read the full blog for free on Medium.

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Published via Towards AI