Hi.
In this video we will learn a little bit about the XG Boost algorithm.
XG Boost is a very, uh,
popular machine learning algorithm I use for classification,
regression and ranking problems. Uh,
it stands for Extreme Gradient Boosting. Uh,
it's very scalable and fast. Uh,
it is an ensemble learning method, uh,
that means that multiple weak decision trees are trained and their
predictions are used to create a strong final prediction. It has,
uh, support for parallel processing, uh,
so we can train g boost on multiple cores and machines.
It also supports outof core computation, uh, which means, uh,
it can perform training on data sets that xts available ram.
It also allows training on GPUs. Uh,
it has a built-in support for handling missing values in the input dataset.
And, uh, while EG Boost does not explicitly remove, uh, variables,
it's process, uh,
of building decision trees involves selecting features that contribute
the most to reducing prediction errors. Exg boot, uh,
has uh, no assumptions on input data distribution.
It also doesn't have any assumption that, uh,
the input features and target are in linear relationship.
And, uh, although outliers, uh, can affect model performance,
uh, XG Boost three base nature makes it, uh, robust, uh,
against them into some extent. Uh, decision trees, uh,
naturally adapt to outliers by creating splits that, uh,
minimize the impact of extreme values. In our next video,
we will create our binary XG Boost model.