A developer has compiled a list of 20 prompts for using large language models (LLMs) in data science tasks, covering model training, hyperparameter tuning, time series forecasting, handling imbalanced…
A new arXiv preprint (arXiv:2609.00071v1) reports that prediction error is not a reliable proxy for causal estimator quality, based on Monte Carlo simulations comparing OLS, GAMs, XGBoost, and Double …
A developer's independent test found that foundation models top TabArena's tabular leaderboard, beating tuned XGBoost 92.6% of the time, but the advantage vanishes when tree-based models receive all t…
A new study on arXiv (2608.28602v1) reports that a LightGBM model achieved around 97 percent accuracy, precision, recall, and F1-score in classifying Parkinson's disease severity using triaxial IMU se…
Researchers introduced actionable case-based feature importance (A-CBFI), a framework for tabular machine learning that integrates structural causal models with counterfactual recourse to target inter…
Researchers introduced the Relational Hypergraph Transformer (RHT), a unified architecture for multi-table learning that represents relational databases as hypergraphs and uses pentadimensional embedd…
A feasibility study on the eICU Demo dataset (2,353 ICU stays, 8.1% mortality) found that a four-step agentic pipeline produced no explanations with explicit outcome leakage, while a standalone LLM pr…
A developer shared a Python script for building a machine learning pipeline for real estate price prediction. The script includes exploratory data analysis, outlier filtering, feature engineering with…
A data engineer with nearly a decade of experience in travel industry data systems detailed the complexities of AI-driven dynamic pricing in hotels, emphasizing the shift from batch to real-time infer…
A developer built a production-grade machine learning system to predict 30-day hospital readmissions using the UCI Diabetes 130-US Hospitals dataset, which contains 101,766 patient encounters from 130…
Block released BlockTabBench2026, an evaluation benchmark of over one hundred datasets from its production machine learning ecosystem, and found that tabular foundation models using in-context learnin…
A developer shared insights from a deep-dive exploratory data analysis on the Ames Housing Dataset, focusing on feature engineering and business logic. The analysis revealed that features like fences …
A new study from arXiv (2608.20406v1) evaluated ARIMA, random forest, and XGBoost models on 190 weekly Ontario COVID-19 case counts from January 2020 to October 2023, finding that no single model domi…
A developer at StratCraft detailed the challenges of building machine learning trading systems, emphasizing that data preparation and validation geometry are more critical than the model training call…
Researchers propose SAGE-XGBoost, a machine learning framework combining noise-based data augmentation with graph embeddings, achieving AUC values of approximately 0.97 for landslide and 0.95 for wild…
Amazon Web Services (AWS) published Part 2 of its no-code machine learning workflow series, detailing how to use Amazon SageMaker Canvas to connect to Snowflake, prepare data with Data Wrangler, and b…
Amazon Web Services (AWS) published Part 3 of its no-code machine learning workflow series, detailing how to visualize fraud detection predictions from Amazon SageMaker Canvas using Amazon Quick Sight…
A developer detailed the integration of machine learning models with live broker data via the Dhan API for a production trading dashboard, focusing on NIFTY options. The system uses XGBoost on 15-minu…
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 new study using connected vehicle telemetry data from Greater Sydney, Australia, found that ARIMA, a classical time-series model, achieves the lowest mean absolute error (MAE: 162.21) in forecasting…