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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.
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!