A developer built a court outcome prediction system that reduced target leakage from 92% to 0.1% across 609,715 Dutch court rulings by stripping outcome-announcing sentences before training. The syste…
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…
Researchers from Princeton University, Ant Group and Stanford University introduced AQuA, a two-part agentic framework for autonomous factor discovery and model development in quantitative finance, wh…
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…
A new study from arXiv (2608.20343v1) presents a bankruptcy prediction framework using hybrid resampling and stacking ensembles with explainable AI, achieving the best minority-class detection with a …
VIDRAFT's FINAL-Bench leaderboard reveals a 0.21 AUROC gap between time-based and random data splits, with a baseline LightGBM model scoring 0.606 AUROC on chronological splits versus 0.818 on random …
Gex.live has launched an MCP server that lets AI assistants like Claude, Cursor, and ChatGPT access its dealer-positioning archive and drive its backtesting Lab. The server provides free tools for rea…
Researchers released ArguLens, an open-source automated essay scoring system that decomposes scoring into three components: a discourse-move classifier, a LightGBM scorer, and a label-aware feedback g…
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…
A new model called the Accounting Graph Transformer (AGT) outperforms existing baselines in forecasting 13 key performance indicators (KPIs) for small businesses with limited accounting history, achie…
Prior Labs' TabPFN, a pre-trained Transformer for tabular data, enables zero-shot machine learning predictions without traditional training, feature engineering, or hyperparameter tuning, according to…
A new study from arXiv (2608.00045v1) finds that textual descriptors alone can predict early-stage startup success, defined as Exit, without relying on contextual, financial, or human capital variable…
Tabular foundation models (TFMs) such as TabPFN from PriorLabs and TabFM from Google are now outperforming gradient-boosted trees like XGBoost, CatBoost, and LightGBM on the TabArena benchmark, challe…
A guide from an unnamed data science source lists seven essential machine learning algorithms that remain relevant despite the rise of large language models and generative AI, arguing that simpler mod…
OpenSquilla's SquillaRouter uses a small on-device machine learning model to route each agent turn to the cheapest capable LLM provider, treating model choice as a live routing problem rather than a s…
Researchers present SIFT (Self-Improving, Frozen-gate Training), a dynamic classifier service that uses a cheap CPU-bound pipeline with a SPLADE sparse encoder and LightGBM head, escalating low-confid…
A benchmark study of time series forecasting models reveals that simple statistical baselines and zero-shot foundation models outperform sophisticated neural and tree-based models on series with stron…
A new machine learning framework from researchers predicts representative clutter height (RCH) for satellite ground station siting using LiDAR-derived terrain data, achieving a mean absolute error of …
Harness-native routing, which optimizes model selection based on the full execution state, is emerging as a key technique for building smarter AI agents, according to a technical analysis. The approac…
A machine learning framework using the IBM Telco Customer Churn dataset achieved 77.68% accuracy and a ROC AUC of 0.8403 in predicting customer churn, with CatBoost as the top performer among three gr…