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Interactive 3D/Active Learning Loop
Active Learning Loop
Strategy: Uncertainty Sampling - Batch: 1 - Step 0
2D Feature Space
Labeled positive
Labeled negative
Unlabeled
Selected for annotation
decision boundary++++−−−−
Accuracy vs Labels Used
Active (uncertainty)
Random baseline
65758595
Labeled
8
Unlabeled
19
Accuracy
98%
Steps
0
Sampling Strategy
Pick the point the model is most unsure about (prob nearest 0.5).
Batch Size
Display
Key Insight
Active learning achieves the same accuracy as random with 5–10× fewer labels by focusing annotation budget on the most informative examples.

Active Learning Loop - Interactive Visualization

Active learning reduces annotation cost by selecting the most informative examples for human labeling - instead of labeling everything. Uncertainty sampling queries the model for points it is least confident about, typically near the decision boundary. Each labeled example shifts the boundary and selects the next batch. This loop achieves high accuracy with 5-10x fewer labels than random sampling.

  • Uncertainty sampling: the model queries points where class probability is closest to 0.5
  • Decision boundary: watch it sharpen with each annotation round as the classifier gains information
  • Label efficiency: reach 90% accuracy with 20 labels using active learning vs 150 with random sampling
  • Query strategies: compare uncertainty sampling, margin sampling, and entropy-based selection

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.