what is overfitting?How to deal with it?

Question

Accepted Answer

Overfitting occurs when a model is too complex and fits the training data too closely, resulting in poor generalization to new data.

Overfitting happens when a model learns the noise in the training data instead of the underlying pattern.
It occurs when the model is too complex or has too many parameters relative to the amount of training data.
Overfitting can be identified by comparing the model's performance on the training data versus a separate validation or test set.
To deal with overfitting, one can use techniques like regularization, cross-validation, or reducing the complexity of the model.
Regularization methods like L1 or L2 regularization add a penalty term to the loss function to discourage complex models.
Cross-validation helps in estimating the model's performance on unseen data by splitting the available data into multiple train-test sets.
Reducing the complexity of the model can involve techniques like feature selection, dimensionality reduction, or using simpler algorithms.

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