What is overfitting in machine learning and how can it be prevented?

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Bhavesh Badani · Answer

Overfitting: Generally Occurs when a model learns the training data too well or too deep in a menner such that it also capture noise and specific details that don’t generalize to new data. Signs of overfitting include high training accuracy but poor performance on unseen data.Ways to prevent overfitting: Regularization: Directly Introduce penalties for complex models (e.g., L1 or L2 regularization), which may regulate overfitting.Early Stopping: Monitor validation performance and stop training as soon as it starts to degrade.Feature Selection: Choose relevant features and avoid unnecessary complexity. This is very helpful step when it comes to prevent overfitting.

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What is overfitting in machine learning and how can it be prevented?

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