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arXiv Machine Learning By Edward Raff, James Holt

Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasets

Read the original on arXiv Machine Learning →

Linear models with an $L_1$-norm penalty are still the leading approach for high‑dimensional tasks, yet many existing solvers are slow, ineffective, and hard to parallelise, making them unsuitable for large industry‑scale corpora. The paper evaluates several recent state‑of‑the‑art methods and shows that older techniques outperform them in general use. It also demonstrates that a simple baseline—LBFGS applied to a sub‑gradient with minor tweaks—yields strong performance and is easier to support and scale in production.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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