#Sklearn
Live, measured metrics for the hashtag #Sklearn from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #sklearn
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · mas.to (Mastodon public tags API) · fetched 2026-09-28 16:56 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mas.to (Mastodon public search API) · fetched 2026-09-28 16:56 UTCNo related tags with measured usage found for #sklearn.
Live pulse
measured · mas.to (Mastodon tag timeline) · fetched 2026-09-28 16:56 UTCEverything below is measured over the latest 40 public posts (spanning ~27526 hours).
Posting hours (UTC) — busiest: 18:00
Languages: English (22) · Russian (11) · Basque (3) · Spanish (3) · Japanese (1)
Avg boosts / post: 1.1
Top of the latest posts
Pipeline в машинном обучении: как создавать сложные модели без боли и утечек данных В ML‑проектах проблемы часто начинаются не с выбора алгоритма, а с предобработки: один трансформер забыли применить к тестовой выборке, другой обучили до кр
Ускоряем и оптимизируем numpy, pandas, scipy и sklearn С момента публикации статьи на Хабре « Импортозамещаем numpy, pandas, scipy и sklearn » прошло почти три года. В течение этого времени я приостановил работу над проектом из-за нехватки
Множественная регрессия: Расширяем горизонты прогнозирования Хотите научиться предсказывать продажи, цены на недвижимость или спрос на товары, учитывая сразу несколько факторов? Вам поможет множественная регрессия. В этой статье вы узнаете:
What “sklearn” means
Wikipediascikit-learn is a free and open-source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project.
“Scikit-learn” on Wikipedia (CC BY-SA) →#sklearn across platforms
every network with a public tag surfaceFollow #sklearn straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/sklearn