A comparative optimization study of logistic regression for fake news detection, evaluating gradient descent, conjugate gradient, Newton, and L-BFGS methods on large-scale TF-IDF text features for convergence behavior, runtime, and memory efficiency.
optimization logistic-regression binary-classification conjugate-gradient-descent kaggle-dataset l-bfgs quasi-newton newton-method fake-news-classification tf-idf-vectorizer convergence-analysis fletcher-reeves polak-ribiere-polyak welfake-dataset
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Updated
Jan 16, 2026 - Jupyter Notebook