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scientific-semi-supervised-learning

Expand labels via self-training, label propagation, and pseudo-labeling.

3|1|Updated Feb 11, 2026
Search Tags:#machine-learning#data-efficiency#openml#semi-supervised#self-training#label-propagation#pseudo-labeling
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-semi-supervised-learning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-semi-supervised-learning
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-semi-supervised-learning
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-semi-supervised-learning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides end-to-end semi-supervised learning pipelines that leverage small amounts of labeled data along with large pools of unlabeled data, enabling model improvement through self-training, label propagation, and pseudo-labeling.

Core Features & Use Cases

  • Self-Training: iteratively expand labeled data by training on confident predictions from unlabeled samples.
  • Label Propagation: graph-based spreading of labels to unlabeled data to improve class coverage.
  • Pseudo-Labeling Quality Evaluation: assess reliability of generated pseudo-labels and tune thresholds for safer labeling.
  • ToolUniverse integration: OpenML benchmarking support for standardized evaluation on external science datasets.

Quick Start

Run a semi-supervised learning workflow by providing your labeled and unlabeled data to iteratively label and improve the model.

Frequently Asked Questions about scientific-semi-supervised-learning

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

FAQPage Schema
How do I train a machine learning model when I only have a small amount of labeled data?▼

Semi-supervised learning solves limited label scarcity by using self-training and pseudo-labeling to iteratively expand your labeled data with confident predictions from unlabeled samples, reducing annotation costs.

What is the best way to evaluate pseudo-labeling quality before adding new data?▼

Evaluating pseudo-labeling quality requires assessing the reliability of generated labels and tuning confidence thresholds to ensure safer label selection during iterative expansion across your datasets.

Does label propagation work for graph-based spreading on small to moderate datasets?▼

Yes, label propagation applies graph-based spreading of labels to unlabeled data, improving class coverage specifically for small-to-moderate datasets where annotation scarcity is an issue.

Can I use OpenML benchmarking to evaluate semi-supervised learning workflows?▼

Yes, OpenML integration provides standardized benchmarking support for evaluating semi-supervised learning pipelines on external science datasets, ensuring robust assessment of your model performance.

How does self-training iteratively expand labeled data in machine learning?▼

Self-training iteratively expands labeled data by training a model on existing labels, generating predictions for unlabeled samples, and adding high-confidence predictions back into the training set.

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