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This paper introduces NLP Psychometrics, a framework that treats psychological prediction from text as a psychometric problem. Using LLM personas, emotional profiles, and syntactic-semantic networks with random forest regressors, it explains up to 76% of variance in mental health scores and shows promise and limits of synthetic data for psychometric prediction.
This paper presents a comparative evaluation of post-hoc analysis methods (Random Forest surrogate, LIME, PCA) for detecting structured patterns in clustering results, using synthetic datasets with injected patterns. It finds that none of the methods consistently detects all pattern types, highlighting a gap in existing explainability tools.
Proposes SymCA, an interpretable column annotation framework using LLMs to materialize annotation as a global-to-local symbolic decision process, achieving significant accuracy improvements over baselines.
CommuniWave is a machine learning model introduced to quantify the Degree of Informal Behavior (DIB) in urban communities using behavior capture, YOLOv10, and random forest, enabling dynamic monitoring for urban managers.
This paper develops a stationary-distribution theory for triplet-based plateau search in Random Forest ensemble-size selection, modeling the central ensemble size as a birth-death Markov chain and deriving equilibrium equations and asymptotic properties.
A critique of a popular quant thread selling a 77% win-rate random forest strategy, noting that the method is standard ensemble learning from a free Stanford lecture and that past performance does not guarantee future results.
This paper presents a hybrid machine learning approach for real-time melt pool monitoring in laser powder bed fusion additive manufacturing, combining EfficientNetB0 feature extraction with Random Forest classification to achieve high accuracy and sub-millisecond inference time.
A researcher describes building a deep learning model with 270k parameters to predict melting points from topological indices, achieving R² 0.6399, and asks whether to publish the results.
This paper applies Random Forest Recursive Feature Elimination to Nigerian household survey data to identify minimal predictors that accurately classify poverty status, quintile distribution, and inequality position, showing that machine learning can reduce data requirements while preserving distributional information for monitoring poverty and inequality.
This paper presents FederatedRSF, a Python package for federated random survival forests that handles partially overlapping medical data across institutions without sharing raw data, and demonstrates comparable performance to centralized training on breast cancer data.
Researchers from Fordham University introduce Reciprocal Co-Training (RCT), a framework that couples LLMs and Random Forest classifiers via reinforcement learning, creating an iterative feedback loop where each model improves using signals from the other. Experiments on three medical datasets show consistent performance gains for both models, demonstrating a general mechanism for integrating incompatible model families.