An engineer detailed the rebuild of an options-buyer prediction system, moving from a flawed V1 XGBoost model to a V2 architecture with separate narrow heads for underlying direction and option contra…
A developer detailed how plain k-fold cross-validation silently overfits options trading models due to sequential data and overlapping triple-barrier labels, and proposed a purged and embargoed split …
A developer detailed the rebuild of an options-buyer prediction system, moving from a single XGBoost model that learned noise from raw premium data to a multi-head architecture that separates underlyi…
A community release called Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-BF16, built on Alibaba's Qwen3.8-27B by the team AEON-7, removes the model's refusal behavior via abliteration and reports a mean KL div…
A fraud detection model that predicts "not fraud" for every transaction scores 98.3% accuracy but is useless, according to a data scientist's account of building a payment-fraud ML pipeline. In the pi…
Senbonzakura, a new open-source tool, removes refusal behavior from open-weight language models by cutting multiple refusal directions in activation space at once, achieving zero hard refusals on Qwen…
A developer demonstrated automatic hyperparameter tuning using Optuna with MLflow and InterSystems IRIS database. The approach efficiently optimizes LightGBM models on the California Housing dataset, …
An arXiv preprint (arXiv:2606.03995) reports an explainable machine learning model that achieved near-perfect three-class classification of Alzheimer's disease, mild cognitive impairment, and normal c…
Researchers developed an XGBoost machine learning model using eight routine clinical features from the Alzheimer's Disease Neuroimaging Initiative dataset that achieved 94.3% accuracy and a 0.982 macr…
The article provides a curated list of open-source Python tools and libraries essential for building algorithmic and quantitative trading systems. Key resources highlighted include Optuna for hyperpar…