#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 · mastodon.social (Mastodon public tags API) · fetched 2026-07-27 01:25 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mastodon.social (Mastodon public search API) · fetched 2026-07-27 01:25 UTCLive pulse
measured · mastodon.social (Mastodon tag timeline) · fetched 2026-07-27 01:25 UTCEverything below is measured over the latest 40 public posts (spanning ~89055 hours).
Posting hours (UTC) — busiest: 18:00
Languages: English (23) · Russian (11) · Basque (3) · French (1) · Japanese (1) · Spanish (1)
Avg boosts / post: 1.3
Top of the latest posts
I ran a quick Gradient Boosted Trees vs Neural Nets check using scikit-learn's dev branch which makes it more convenient to work with tabular datasets with mixed numerical and categorical features data (e.g. the Adult Census dataset). Let's
When training a model it turns out that I get better results with a small dataset than with a bigger dataset. This is what is called overfiting, right? #MachineLearning #Sklearn
Dear Machine Learning people: when a problem can be solved using both a regressor and a classifier, which method would you choose? Or you simply try both and then choose whatever worked better? Any rule or set of rules to try to determine w
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