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XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
Starting from version 1.5, XGBoost has experimental support for categorical data available for public testing. For numerical data, the split condition is defined as value \u003C threshold, while for categorical data the split is defined depending on whether partitioning or onehot encoding is used.
Starting from version 1.5, XGBoost has experimental support for categorical data available for public testing. For numerical data, the split condition is defined as value \u003C threshold, while for categorical data the split is defined depending on whether partitioning or onehot encoding is used.
Mentions in press and media 35
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| Feb 18, 2026 | Использование термодинамической помощи случайным лесам для многоклассификационных задач (с кодом Python) | Метод SHAP (SHapley Additive exPlanations), опирающийся на классическую теорию игр, утвердился в качестве стандарта для оценки значимости признаков в моделях машинного обучения. В задачах бинарной классификации процесс построения графиков w... |
| Jun 3, 2025 | Literal Labs: A New Dawn for Energy-Efficient AI | In the bustling world of artificial intelligence, a new player has emerged, promising to change the game. Literal Labs, a startup hailing from Newcastle, UK, has secured £4.6 million in Pre-Seed funding. This financial boost marks a signifi... |
| Jun 2, 2025 | Literal Labs raises £4.6M to advance faster, energy-efficient AI models | AI startup Literal Labs has raised £4.6 million in Pre-Seed funding. Literal Labs is pioneering logic-based AI models that are orders of magnitude faster, more energy efficient, and more explainable than today’s neural networks. Its approac... |
| May 3, 2025 | Building a Safer Future: How Predictive Analytics is Transforming Construction Safety | Every day, construction sites pulse with activity. Heavy machinery roars, workers hustle, and structures rise. Yet, lurking beneath this industrious facade is a grim reality. Each workday, three construction workers in the United States fai... |
| Apr 30, 2025 | Predictive Analytics: The Tech Industry’s Secret Weapon Against Construction Injuries | Share Share Share Share Email Why Construction Safety Needs a Data Upgrade Now Every workday, an average of three construction workers in the United States don’t make it home. In 2023 the industry logged roughly 1,075 on-site fatalities—abo... |
| Feb 7, 2025 | From 220M data points to revenue: How AI is transforming sports entertainment ROI | Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The Super Bowl is one of the largest sports entertainment events on the planet, bringing in more than a hundred m... |
| Dec 31, 2024 | A-Tune: The Future of Server Optimization with Machine Learning | In the world of server management, performance is king. Imagine a race car. It can only go as fast as its engine allows. Similarly, server applications can only perform as well as their underlying systems permit. Enter A-Tune, a tool that p... |
| Dec 26, 2024 | A-Tune: тонкая настройка системы с использованием машинного обучения | Привет, Хабр! Меня зовут Артём, я инженер-программист в департаменте серверных решений. В статье расскажу про новый инструмент для повышения производительности, который получилось портировать и доработать для ОС Astra Linux Special Edition.... |
| Dec 24, 2024 | Сравнение алгоритмов градиентного бустинга или история знает только первых… | Всем привет! Данная статья написана по итогам обучения на курсе Otus ML Basic и в ней я проведу сравнение алгоритмов градиентного бустинга. Почему бустинг, спросите вы ? Понятно, что нейронные сети интереснее, но не всегда их применение цел... |
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FAQ about XGBoost Documentation
What has the press written about XGBoost Documentation?
Parsers VC has collected 35 articles mentioning XGBoost Documentation, among them “Использование термодинамической помощи случайным лесам для многоклассификационных задач (с кодом Python)” (Feb 18, 2026) and “Literal Labs: A New Dawn for Energy-Efficient AI” (Jun 3, 2025).
Is XGBoost Documentation still active?
Yes, XGBoost Documentation is active; its latest activity on Parsers VC dates from Mar 9, 2026.