suresh In machine learning models, overfitting can be handled using techniques such as:
1. Cross-Validation: Use techniques like k-fold cross-validation to evaluate the model's performance on multiple subsets of the data.
2. Regularization: Apply regularization techniques like L1 (Lasso) and L2 (Ridge) regularization to penalize large coefficients and prevent overfitting.
3. Feature Selection: Choose relevant features and eliminate irrelevant ones to simplify the model and reduce overfitting.
4. Early Stopping: Monitor the model's performance on a validation set during training and stop training when the performance starts to degrade.
5. Ensembling: Combine multiple models (such as Random Forests or Gradient Boosting) to reduce overfitting and improve generalization.
By implementing these strategies, you can effectively handle overfitting in machine learning models and improve their performance.
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