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The paper investigates whether guideline-based categorical encodings of continuous predictors can replace continuous inputs in stroke outcome prediction models without sacrificing accuracy, finding comparable performance in most treatment cohorts.
path_boost is a Python package implementing PathBoost, a gradient boosting algorithm for interpretable graph-level prediction. It automatically discovers predictive labeled paths in graphs, supports regression and binary classification, and is compatible with scikit-learn.
A Kaggle Grandmaster is offering a free course covering the entire machine learning process, including EDA, decision trees, gradient boosting, feature engineering, time series, and competition strategies.
This paper investigates when activation steering succeeds or fails for LLMs by analyzing early decoding dynamics. The authors introduce ASTEER, a large testbed of steered generations, and train a GBDT classifier to predict steering outcomes from early hidden states, enabling efficient steering strength search.
This paper presents LiverRisk, a machine learning framework for NAFLD risk prediction that combines gradient-boosted decision trees with conformal prediction to provide calibrated, distribution-free coverage guarantees on individual risk estimates, achieving high AUROC on internal and external cohorts.
This paper introduces a method to predict activation steering effectiveness in language models from early decoding states using a Gradient Boosting Decision Trees (GBDT) classifier, enabling efficient steering strength optimization without full rollouts.
This paper presents a hybrid architecture combining FT-Transformer with gradient-boosted trees via calibration-aware stacking for customer churn prediction on structured tabular data, achieving improved F1 and AUC-ROC on a public bank churn dataset.
A blog post from Flyback demonstrates how a LightGBM feature that ranked #1 in importance actually worsened predictions due to target encoding leakage, highlighting the danger of relying solely on feature importance metrics.
This paper introduces PathBoost, a gradient tree boosting method for graph-level prediction that uses path-based features to compete with graph neural networks while offering better interpretability.