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The article details building a machine learning model using logistic regression to predict winners and eliminations in the TV show Survivor, with an interactive dashboard for visualization.
The article discusses the limitations of using LLMs directly as classifiers and proposes treating LLM outputs as features in traditional machine learning models like logistic regression to achieve better calibration, interpretability, and performance.
This study compares mutual information and data-based sensitivity analysis for feature selection in bank telemarketing, showing that sensitivity analysis achieves good prediction with fewer features while mutual information is better for cost reduction.
This theoretical paper studies the non-asymptotic implicit bias of logistic regression under gradient descent, proving that the parameter vector weakly aligns with the max-margin direction quickly, within a doubly exponential number of iterations in the alignment error.
This paper investigates the high false positive rate of existing AI-text detectors when applied to patent claims, which are legally required to be clear and concise like LLM output, and proposes a logistic regression classifier based on linguistic complexity features that outperforms perplexity-based detectors on consumer hardware.