skills.rest
nahisaho
nahisahoCOMMUNITY
Explore all skills by this author →
GitHub

scientific-active-learning

Design and execute active-learning pipelines with uncertainty sampling and OpenML datasets.

3|1|Updated Feb 11, 2026
Search Tags:#machine-learning#active-learning#model-improvement#uncertainty-sampling#query-by-committee#data-pool
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-active-learning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-active-learning
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-active-learning
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-active-learning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Designs, implements, and evaluates active-learning pipelines to minimize labeling costs while maximizing model accuracy.

Core Features & Use Cases

  • Uncertainty sampling for informative labeling
  • Query-by-Committee and other disagreement-based strategies
  • Batch active learning and pool/stream sampling
  • Stopping criteria and convergence monitoring

Quick Start

Run the active-learning loop with your labeled data, unlabeled pool, and test set to iteratively improve a classifier.

Frequently Asked Questions about scientific-active-learning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce labeling costs for machine learning with limited data?▼

Uncertainty sampling is an active learning strategy that queries the unlabeled data points your model is least confident about. It ensures annotators only label informative samples, accelerating convergence.

What is the difference between uncertainty sampling and Query-by-Committee?▼

Batch active learning selects a batch of diverse unlabeled samples simultaneously rather than querying one by one. This parallelizes the annotation process and significantly reduces active learning iteration overhead.

How do I set stopping criteria for an active learning loop?▼

Stopping criteria halt the active learning loop when model accuracy convergence is reached or labeling budgets are exhausted. This prevents unnecessary annotation of redundant data points.

Does this active learning pipeline support streaming unlabeled data?▼

Yes, the active learning pipeline supports both pool-based and streaming unlabeled data scenarios. It dynamically evaluates and queries incoming data streams to continuously optimize the classifier.

Related Skills

Explore More Data & Analytics Skills→
SerjSmorSerjSmor
5

active-learning

Coordinate active-learning loops for low-label classifier tasks with Argilla annotation.

Community
Advanced
KameniAlexNeaKameniAlexNea

adversarial-validation

Detect and quantify distribution shift between training and testing datasets using a LightGBM classifier.

Community
Intermediate
Nir-BhayNir-Bhay
19

active-learning-system

I'm not sure. I'm just a computer programulum.

Community
Advanced
OlixIgnaciousOlixIgnacious
1

kaggle-pseudo-labeling

Apply semi-supervised learning to retrain Kaggle models with pseudo-labels from test data.

Community
Advanced