Question: What is the difference between overfitting and underfitting in machine learning? Overfitting occurs when the model performs well on training data but poorly on test

What is the difference between overfitting and underfitting in machine learning?

Overfitting occurs when the model performs well on training data but poorly on test data, while underfitting occurs when the model performs poorly on both training and test data.

Overfitting occurs when the model is too simple, while underfitting occurs when the model is too complex.

Overfitting occurs when the model has high variance, while underfitting occurs when the model has high bias.

Overfitting occurs when the model is biased, while underfitting occurs when the model is unbiased.

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