Data Science: The AdaBoost Algorithm and its Theory.

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This story is part of my Data Science series.

In general boosting is a way of improving a supervised learner’s strength by recursively building a weighted sum of trained models. It is not restricted to a specific model type, but can be applied to any classification or regression model. Though, in this small account we will focus on classification problems only.

Algorithm:

This shall be enough for now. In the next account we will look into a possible implementation of AdaBoost in Rust.

Thank you for reading!