Catboost
PulseAugur coverage of Catboost — every cluster mentioning Catboost across labs, papers, and developer communities, ranked by signal.
5 天有情绪数据
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Machine learning predicts heart disease from CT scans
Researchers have developed a machine learning framework to predict obstructive coronary artery disease (CAD) using CT scans. The model analyzes features from coronary calcium and epicardial fat, identifying 14 key predi…
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Tabular foundation models show promise for NIR chemical sensing calibration
Researchers have explored the use of tabular foundation models, specifically TabPFN, as a novel calibration strategy for near-infrared (NIR) chemical sensing. In a study involving 66 NIR datasets, TabPFN demonstrated st…
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CatBoost ML Interview Prep: 25 Q&A Guide
This article provides a collection of 25 question-and-answer pairs designed to help individuals prepare for machine learning interviews, specifically focusing on the CatBoost algorithm. It aims to build confidence in ca…
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Researchers distill large AI models into faster CPU-ready gradient-boosted trees
Researchers have developed a method to distill large tabular foundation models (TFMs) into smaller, faster gradient-boosted tree models that can run on CPUs. This technique addresses the latency issue of TFMs, which are…
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TabH2O foundation model unifies tabular prediction tasks
Researchers have introduced TabH2O, a novel foundation model designed for tabular data prediction tasks like classification and regression. This model utilizes a unified training approach with a dual-head architecture, …
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Shipping logistics boosted by new retrieval-enhanced Transformer model
Researchers have developed a novel deep learning framework called CCRE to improve multi-step port-of-call sequence prediction in global shipping logistics. This framework utilizes a retrieval-enhanced historical encoder…
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New calibration framework streamlines NIRS spectral preprocessing
Researchers have developed a new framework called operator-adaptive calibration to streamline the selection of spectral preprocessing methods in near-infrared spectroscopy (NIRS). This approach integrates preprocessing …
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Guide Explains Tree-Based Models From Decision Trees to Boosting
This article provides a guide to tree-based models, explaining their effectiveness with tabular data and their evolution from simple decision trees to advanced boosting algorithms like XGBoost, LightGBM, and CatBoost. I…
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Manokhin Probability Matrix offers new framework for classifier quality
Researchers have introduced the Manokhin Probability Matrix, a new diagnostic framework designed to evaluate the quality of probabilistic predictions from classifiers. This framework separates reliability and resolution…
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New benchmarks improve IBD classification using donor-aware scRNA-seq analysis
Researchers have developed a donor-aware benchmark for classifying Inflammatory Bowel Disease (IBD) using single-cell RNA sequencing (scRNA-seq) data. This new benchmark addresses the issue of pseudoreplication by ensur…
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New 'Orange Book of Machine Learning' covers supervised regression and classification
A new book titled "The Orange Book of Machine Learning - Green edition" has been released, focusing on supervised regression and classification for tabular data. Authored by Carl McBride Ellis, the book covers essential…
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ML models show difficulty forecasting volatile Australian electricity prices
A new study benchmarks six machine learning models for short-term electricity price forecasting in Australia's National Electricity Market. The research highlights significant challenges due to high price volatility, ir…
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An Integrated Framework for Explainable, Fair, and Observable Hospital Readmission Prediction: Development and Validation on MIMIC-IV
Researchers have developed a new gradient-regularized Newton scheme to ensure global convergence for Gradient Boosting Decision Trees (GBDTs), a technique widely used in tabular machine learning. This method introduces …