In each competition, numerous practitioners repeatedly evaluated their progress against a holdout set that forms the basis of a public ranking availablethroughout the competition. Performance on a separate test set used only oncedetermined the final ranking.

Inanutshell, features ofthis dataset are sampled randomly fromN(0,1), and the target is produced by an ensemble of randomly constructed decision trees applied to the sampledfeatures. Our dataset has10,000 objects, 8 features and the target was produced by16 decision trees of depth6. CatBoost is trained with the default hyperparameters. Importantly,thelattermeans that this approach is not covered by the embedding framework described in subsection 3.1. So, it seems to be important to embed each feature separately as describedinsubsection3.1.

Moreover,bothframeworks are homogeneous: the hypothesis class is fixed at each boosting iteration.

Since 2010, Kaggle has been a platform where data scientists from around the world come together to compete, collaborate, and push the boundaries of Data Science. Over these 15 years, it has grown from a purely competition-focused site into a broader ecosystem with forums, notebooks, models, datasets, and more. With the release of the Kaggle Meta Code and Kaggle Meta Datasets, we now have a unique opportunity to explore these competitions, technologies, and real-world applications of Machine Learning and AI. And so in this study, we take a closer look at 15 years of data science on Kaggle - through metadata, shared code, community discussions, and the competitions themselves. We explore Kaggle's growth, its impact on the data science community, uncover hidden technological trends, analyze competition winners, how Kagglers approach problems in general, and more. We do this by analyzing millions of kernels and discussion threads to perform both longitudinal trend analysis and standard exploratory data analysis. Our findings show that Kaggle is a steadily growing platform with increasingly diverse use cases, and that Kagglers are quick to adapt to new trends and apply them to real-world challenges, while producing - on average - models with solid generalization capabilities. We also offer a snapshot of the platform as a whole, highlighting its history and technological evolution. Finally, this study is accompanied by a video (https://www.youtube.com/watch?v=YVOV9bIUNrM) and a Kaggle write-up (https://kaggle.com/competitions/meta-kaggle-hackathon/writeups/kaggle-chronicles-15-years-of-competitions-communi) for your convenience.

We introduce Falcon, a cross-domain Chinese text-to-SQL benchmark grounded in an enterprise-compatible dialect (MaxCompute/Hive). It contains 600 Chinese questions over 28 databases; 77% require multi-table reasoning and over half touch more than four tables. Each example is annotated along SQL-computation features and Chinese semantics. For evaluation, we release a robust execution comparator and an automated evaluation pipeline, under which all current state-of-the-art large-scale models (including Deepseek) achieve accuracies of at most 50%. Major errors originate from two sources: (1) schema linking in large enterprise landscapes - hundreds of tables, denormalized fields, ambiguous column names, implicit foreign-key relations and domain-specific synonyms that make correct join/column selection difficult; and (2) mapping concise, colloquial Chinese into the exact operators and predicates required for analytics - e.g., choosing the correct aggregation and group-by keys, expressing time windows and granularities, applying unit conversions, handling NULLs and data-quality rules, and formulating nested or windowed subqueries. Falcon therefore targets Chinese-specific semantics and enterprise dialects (abbreviations, business jargon, fuzzy entity references) and provides a reproducible middle ground before full production deployment by using realistic enterprise schemas, query templates, an execution comparator, and an automated evaluation pipeline for end-to-end validation.

On HuggingFace, we find information about the annotation creators ( e.g., crowdsource, experts, ml-generated) or specific task categories ( e.g., image-classification, image-to-text, text-to-image). Kaggle automatically computes a usability score, which is associated with the tag "well-documented", Kaggle's usability score is based on: Completeness: subtitle, tag, description, cover image . Credibility: provenance, public noteboook, update frequency . Compatibility: license, file format, file description, column description . The usability score is based on only 4 out of 7 aspects from Datasheets [40].