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Why Training Performance Alone Is Not Enough

Why Training Performance Alone Is Not Enough

A common mistake in machine learning is evaluating a model only on training data.

A model may achieve excellent results on training data simply because it has memorized the examples.

This problem is known as overfitting.

To determine whether the model can generalize to new data, we must evaluate it using the testing dataset.