Reducing Human Annotation with ML Active Learning
Machine Learning Reducing Human Annotation with ML Active Learning In a world where human time is expensive, learn how to use it only when really necessary Lucas Braga Jul 27, 2026 20 min read Share Active learning aims to select the unlabeled samples that are most informative and ask for labels only for those samples. By interactively querying a user (or some other information source) to label new data points, we can train models using less labels. This tutorial/tutorial walks you through the active learning workflow and shows you how to implement three commonly used query strategies: uncertainty sampling, diversity based sampling and query by committee.
- ▪Machine Learning Reducing Human Annotation with ML Active Learning In a world where human time is expensive, learn how to use it only when really necessary Lucas Braga Jul 27, 2026 20 min read Share Active learning aims to select the unlabe
- ▪By interactively querying a user (or some other information source) to label new data points, we can train models using less labels.
- ▪This tutorial/tutorial walks you through the active learning workflow and shows you how to implement three commonly used query strategies: uncertainty sampling, diversity based sampling and query by committee.
Towards Data Science files mainly under ai. We currently carry 82 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/reducing-human-annotation-with-ml-active-learning/ |
| Publication time | Mon, 27 Jul 2026 15:00:00 +0000 |
| Retrieval time | 2026-07-27T15:16:24.285Z |
| Last seen | 2026-07-27T15:16:24.285Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 82zsOwMaJZPx · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Machine Learning Reducing Human Annotation with ML Active Learning In a world where human time is expensive, learn how to use it only when really necessary Lucas Braga Jul 27, 2026 20 min read Share Active learning aims to select the unlabeled samples that are most informative and ask for labels only for those samples. By interactively querying a user (or some other information source) to label new data points, we can train models using less labels. This tutorial/tutorial walks you through the active learning workflow and shows you how to implement three commonly used query strategies: uncertainty sampling, diversity based sampling and query by committee. We provide intuitive explanations of these methods along with Python implementations.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
Discussion
0 commentsMore from Towards Data Science
