What is overfitting and why avoid it?

How It Works:

Overfitting happens when a model captures noise in training data, performing well on seen samples but poorly on new data.

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Key Benefits of Avoidance:

• Generalization: Better performance on real-world inputs.
• Reliability: Predictable behavior outside the training set.
• Maintainability: Less need for constant retraining.

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Real-World Use Cases:

• Customer churn: Models generalize to new customer segments.
• Image recognition: Avoid spurious pixel patterns.