Tag: #tabular-data10 results
Entropic Scree is an open-source, model-agnostic diagnostic that uses Normalized Mutual Information to estimate intrinsic rank in complex tabular datasets. It aims to reduce the overestimation and structural failures associated with PCA, Kernel PCA, and Euclidean nearest-neighbor estimators.
Zer0Fit is a new MCP server that allows developers to run Google's TabFM and TimesFM foundation models locally within a Docker container. It enables zero-shot machine learning tasks like forecasting and classification directly through LLM interfaces like Open WebUI.
Google Research introduced TabFM, a foundation model that treats tabular prediction as an in-context learning problem. It eliminates the need for per-dataset training, hyperparameter tuning, and complex feature engineering pipelines.
MoCA-Agent is a new framework that replaces multi-agent debate with a market-based verification system to improve accuracy in financial and tabular reasoning. By decomposing questions into atomic claims and using specialist agents to trade on their validity, the system synthesizes more reliable Python-based solutions.
TabFM Studio is a new web-based tool that allows users to run Google's TabFM tabular foundation models locally on spreadsheets. It enables non-programmers to perform predictions by simply selecting columns and using existing data as in-context examples.
NVIDIA explores the development of foundation models specifically for financial transaction data. This approach aims to move beyond brittle, hand-engineered features by leveraging the sequential patterns inherent in customer history.
Fundamental’s NEXUS, a specialized large tabular model, is now available for deployment via Amazon SageMaker JumpStart. This integration allows enterprise users to easily deploy the model and run inference on their own datasets.
ReSS framework extracts decision paths from decision trees as symbolic scaffolds to guide LLMs in generating faithful reasoning for tabular data. It creates a high-quality dataset for fine-tuning LLMs, augmented for better generalization. Achieves up to 10% gains on medical/financial benchmarks with new faithfulness metrics.
A new autonomous ML agent, inspired by Karpathy's AutoResearch, uses Claude to indefinitely run experiments on tabular binary classification tasks. It analyzes data, hypothesizes, edits code in three locked files, evaluates with temporal splits, and logs insights via git. Key lessons emphasize airtight evaluation and experiment throughput.
Researchers compared statistical, ML, and deep learning models on 18,792 US children aged 10-17 from 2021 NSCH to predict overweight/obesity. AUC ranged 0.66-0.79, with logistic regression, gradient boosting, and MLP offering the best balance of discrimination and calibration. Complex models provided limited gains, and subgroup disparities persisted across races and poverty levels.