Kaggle

The Titanic dataset is widely known through the introductory competition on the Kaggle platform, "Titanic: Machine Learning from Disaster".

在Kaggle等平台上，XGBoost、LightGBM和CatBoost几乎垄断了结构化数据竞赛的顶级解决方案

Gradient Boosting算法长期主导Kaggle结构化/表格数据竞赛排行榜，被认为是非图像、非文本数据的默认最优选择

The models tested include Google's Gemini 3.7 Flash, Z.ai's GLM-5, Alibaba's Qwen 3 235B A22B Instruct, and OpenAI's gpt-oss-120b, all running on Kaggle.

A complete end-to-end project covering data collection, cleaning, training, evaluation, deployment, and monitoring is more convincing than ten Kaggle notebooks that stop at the modeling stage.

The complete code, scorer tests, and raw responses have been open-sourced on GitHub, with a corresponding benchmark and leaderboard on Kaggle.

Kaggle's beginner competitions, open-source projects on GitHub, and Reddit's r/learnmachinelearning are all good channels for getting feedback.

In the Kaggle benchmark challenge, the authors proposed the failure mode of LLM State Reconciliation: when a model maintains a user's structured state across multi-turn conversations, it may make correct changes while simultaneously altering fields that should not be touched.

The researchers queried Kaggle's 46-model catalog, but the first expansion round exhausted the daily AI quota, and the final paired analysis included 7 endpoints that completed all 90 image prompts: Claude Haiku 4.5/5.5, Claude Sonnet 5/5.5, Gemini 3.5 Flash-Lite, Gemini 3.7 Flash, GPT-5.4 Mini.

The Resolve benchmark is publicly available on Kaggle, version 1, under the Apache 2.0 license.

The developer Aditya Kumar Puri published an evaluation set named Resolve in the Kaggle community benchmark challenge, specifically measuring the 'half-fix' problem in code editing (completing the required change but losing old behavior that was not restated).

FairScreen is a project built by developer Maksim Silchenko for a Kaggle benchmarking challenge, using ten LLMs to conduct systematic testing on fictional resumes with known answers in order to evaluate LLM hiring screening decisions.

For gpt-oss-120b on the same 48 core families, the pass rate was approximately 63% on the Kaggle platform and as high as 98% on the cluster; the discrepancy stems from hosted answers being truncated mid-word, and on the 31 families where hosted answers were complete, both platforms passed 30.

OpenAI gpt-oss-120b timed out twice within a 19-hour testing window, and IBM Granite 4.0 was not included in the test because it was not in the Kaggle server catalog.

TurnBasedBenchmarks is a testbed built for Kaggle benchmark challenges that tests the strategic resilience of language models using two turn-based games: rock-paper-scissors and tic-tac-toe.

GPT-5.5 ran out of usage quota, leaving 35 of the 90 questions unfinished; all 55 answered questions were correct, but Kaggle counted the unanswered questions as wrong, causing its official score to be lower.

The challenge organizers confirmed that external APIs and cached results do not satisfy the 'platform-run' requirement of this challenge, and the developer acknowledged that no publicly accessible Kaggle benchmark URL or successful native Kaggle results currently exist.

In the benchmark, each optimizer attempt is limited to 8 tool calls, at most 6 successful edits, and 2 benchmark feedback calls, with a 600-second guard, and modifications to weights, tokenizer, stopping rules, threads, and external libraries are prohibited.

An offline audit of a Kaggle prompt injection evaluation built score auditing tools for three public Kaggle tasks to verify the true meaning of existing benchmark scores, rather than re-running models or training new systems.

MLE-bench is a standardized benchmark released by OpenAI in 2024, curating 75 historical competition tasks from Kaggle spanning tabular data, computer vision, natural language processing, and other domains, with scoring benchmarked against the Kaggle medal system.