Serving Spam Detection With XGBoost and Elixir

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Serving Spam Detection With XGBoost and Elixir
The article by Andrés Alejos explains the process of creating a spam detection system using XGBoost and Elixir. It begins with an introduction to decision trees powered by Nx and details the steps necessary for setting up the Elixir environment with relevant dependencies like ExGBoost and EXLA. The author also provides a glimpse into the TF-IDF vectorization process and how to transform email data into a format suitable for tree-based algorithms. A significant part of the article addresses training an EXGBoost model for binary classification of emails and tuning the model for accurate spam detection. Additionally, the content covers compiling the trained model into an efficient function using Mockingjay and demonstrates how to serve the model in a production environment using Nx.Serving. The author integrates all these components into an interactive applet within a Livebook session, showcasing how the spam detection system performs predictions on new data entries.

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