Overfitting: Why Your Model Fails in Production
98% accuracy in training. 65% on new data. That's overfitting.
Your model memorized the training data instead of learning patterns. It fit the noise, not the signal.
How You Know
Training improves. Validation gets worse. That gap is the problem.
What Fixes It
More data. Best fix. Harder to memorize with more examples.
Simpler models. Fewer features, shallower trees, less complexity.
Regularization. L1, L2, dropout. Punishes complexity.
Cross-validation. Test on multiple splits. Wildly different scores = overfitting.
Bottom line: Training accuracy doesn't matter. Only real-world performance counts.
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