Below is the same dataset. But watch what happens when you toggle labels on and off.
⚠️ You MUST try all 4 modes
This lesson won't make sense until you see what happens with wrong labelsand removed labels. Don't skip ahead!
🔍 Without Labels
Model sees patterns (gray circles show natural groupings) but has NO GOAL. It can cluster, but doesn't know what's "right" or "wrong".
Behavior: Groups similar things together, but can't predict labels.
Try all four modes to unlock the insight →
What You Just Saw
Labels don't just describe the data — they steer what the model learns.
- • With correct labels: Model learns the actual pattern
- • With wrong labels: Model learns the wrong pattern confidently
- • Without labels: Model has no direction at all
💡 The Deep Implication
This is why data labeling is so important. Whoever creates the labels determines what the model will learn. The "correct" answer is a human choice, not an objective truth.