I believe that machine learning (ML) in Julia has developed sufficiently to warrant its own dedicated space, especially with the evolution of libraries like Flux, MLJ, and JuliaML, which have progressed beyond traditional statistical workflows. While many ML methods draw from statistics, their goals differ focusing on generalization, scalability, and optimization, among others. This divergence can sometimes lead to confusion in discussions. However, maintaining some overlap in terminology and collaborative projects could benefit the community as a whole.
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