How would you handle overfitting in a machine learning model?

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How to Handle Overfitting in a Machine Learning Model

Overfitting is a common issue in machine learning models that occurs when the model performs well on the training data but poorly on new, unseen data. There are several ways to address overfitting:

1. Cross-Validation: Use techniques like k-fold cross-validation to validate the model's performance on different subsets of data.
2. Feature Selection: Carefully select relevant features and remove irrelevant ones to reduce model complexity.
3. Regularization: Introduce regularization techniques like L1 and L2 regularization to penalize complex models.
4. Early Stopping: Stop training the model when the performance on the validation set starts to decrease.
5. Ensemble Methods: Combine multiple models to reduce overfitting and improve generalization.

By implementing these strategies, you can effectively mitigate overfitting in your machine learning model and achieve better performance on new data.