Catboost
PulseAugur coverage of Catboost — every cluster mentioning Catboost across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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Tabular Deep Learning Models Compared to Classical ML for Land Cover Classification
A new research paper compares the effectiveness of tabular deep learning (TDL) models against classical machine learning algorithms for urban land cover classification. The study utilized the ULC dataset from the UCI Ma…
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Foundation models show mixed results for pedestrian crowd forecasting
A new study published on arXiv evaluates the effectiveness of time-series foundation models (FMs) for pedestrian crowd count forecasting. The research compares seven different forecasting approaches, including tradition…
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Machine learning model improves grid job scheduling efficiency
A new research paper published on arXiv details a machine learning approach for predicting job runtimes in grid computing environments. The study focuses on leakage-safe, scheduler-aware prediction using the GWA-T-4 Auv…
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Foundation models require fine-tuning for diabetes glucose forecasting
A new study published on arXiv evaluates the effectiveness of time-series foundation models for continuous glucose monitoring (CGM) forecasting, particularly for individuals with type 1 and type 2 diabetes. The research…
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B2B Customer Conversion Prediction Achieves 91% Accuracy with New Methodology
Researchers have developed a new methodology for predicting B2B customer conversion, achieving 91% accuracy. The approach involves aggregating individual contacts to the B2B customer level, generating features, and then…
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Customer support recommender system migrates from gradient-boosted trees to deep learning
A research paper details the migration of a production customer support recommender system from a gradient-boosted tree model to a deep recommender architecture. The migration was necessary due to evolving product catal…
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New qshap tool decomposes R-squared for gradient-boosted trees
Researchers have introduced qshap, a new method for decomposing R-squared values in gradient-boosted decision trees. This tool, available in R and Python, quantifies the contribution of individual features to overall mo…
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LightGBM fails to fit interaction data where CatBoost succeeds
A user on Reddit's r/MachineLearning subreddit is seeking to understand why LightGBM struggles to fit a simple toy dataset that models interaction effects, while CatBoost handles it perfectly. The user's experiment invo…
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AI model predicts male domestic violence in Bangladesh using explainable learning
Researchers have developed an explainable ensemble learning model to predict male domestic violence (MDV) in Bangladesh. The study addresses challenges of imbalanced data and limited availability by collecting data from…
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LLMs applied to flight safety analysis with new FlightLLM approach
Researchers have developed FlightLLM, a novel approach using large language models (LLMs) to interpret flight safety events. This method addresses challenges like modal inconsistency and limited task-specific data by co…
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New research reveals surprising generalization in tabular foundation models
New research explores the surprising generalization capabilities of tabular foundation models (TFMs), suggesting that strong transfer learning can be achieved even from self-supervised pre-training on a single real tabl…
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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. This model unifies classification and regression into a single forward pass using in-context learning, improving t…
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New AI Framework Enhances Private Market Company Valuation
Researchers have developed a novel framework using ensemble tree-based supervised similarity learning to identify comparable companies in private markets. This approach leverages a CatBoost model trained on private comp…
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Machine learning models show accuracy drop with limited residential energy data
A new study published on arXiv compares the effectiveness of various machine learning models for estimating residential energy consumption using limited input data. Researchers found that while models like CatBoost achi…
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New framework offers interpretable AI for sepsis prediction
Researchers have developed a novel framework for modeling sepsis using temporal electronic health record (EHR) data. This approach prioritizes interpretability by design, representing data relationally and then proposit…
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New 'split-candidate scaling' parameter reveals double descent in GBDTs
Researchers have identified a new capacity parameter for gradient boosting decision trees (GBDTs) called split-candidate scaling, which can lead to a phenomenon known as double descent. Unlike neural networks, GBDTs hav…
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AI system predicts football outcomes using tactical profiles, outperforming traditional methods
Researchers have developed Sim2Win, a novel framework for predicting football match outcomes and profiling team tactics without relying on team names or identities. This system utilizes event-based data to construct tac…
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Tabular Foundation Models Show Promise but Face Deployment Hurdles
Recent research indicates that Tabular Foundation Models (TFMs), such as TabPFN and TabFM, are showing strong performance on tabular machine learning tasks, sometimes surpassing traditional gradient-boosted models like …
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New FALCON-Discover framework identifies dangerous overconfidence in AI predictions
Researchers have introduced FALCON-Discover, a new post-hoc framework designed to identify and rank predictions that exhibit false confidence. This method focuses on discovering concentrated regions of overconfident err…
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Machine learning models benchmarked for electricity demand forecasting
A new benchmark study evaluated ten machine learning models for short-term electricity demand forecasting in New England, utilizing weather, calendar, and COVID-19 data. The research found that gradient-boosted tree mod…