Lesson 10 of 13
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Regularization is one of the most important techniques for preventing overfitting in machine learning. When a model is too flexible, it can memorize the training data, including its noise and random quirks, rather than learning genuine patterns. This leads to excellent performance on training data but poor performance on new data.
Regularization is one of the most important techniques for preventing overfitting in machine learning. When a model is too flexible, it can memorize the training data, including its noise and random quirks, rather than learning genuine patterns. This leads to excellent performance on training data but poor performance on new data.