Neura saysSupervised learning trains on labeled examples — input paired with the correct answer.
You give the model inputs and their known outputs (labels): images tagged "cat"/"dog", emails marked spam/not. It learns to predict the label for new inputs. It's the most common, most reliable paradigm — but it needs labeled data, which is expensive to produce. Classification (discrete labels) and regression (continuous values) are its two flavors.
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Trains on (input, correct-label) pairs
Predicts labels for new inputs
Needs labeled data (costly)
Classification + regression
The Overfit Ogre attacks — common mistakes
Underestimating labeling cost/effort
Label leakage inflating accuracy
Assuming labels are always correct
Boss battleGive one classification and one regression example.
Example code
<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>labeled data: (image, "cat"), (image, "dog") ...
→ model predicts label for a new image
classification: cat/dog · regression: house price</pre></body></html>