What are techniques to reduce Overfitting?

 

Method	Description
More Data	Increase the size of the training dataset.
Cross-Validation	Assess the model's performance using k-fold cross-validation.
Feature Selection	Carefully choose relevant features and exclude irrelevant ones.
Feature Engineering	Engineer meaningful features that capture essential information.
Simpler Models	Opt for simpler models with fewer parameters when possible.
Regularization	Apply L1 or L2 regularization to penalize large parameter values.
Dropout	In neural networks, randomly set a fraction of neurons to zero during training.
Early Stopping	Monitor validation performance and stop training when it degrades.
Ensemble Learning	Use ensemble methods like random forests or gradient boosting.
Pruning (Decision Trees)	Remove branches that do not significantly contribute to predictive power.
Cross-Feature Validation	Validate models on data from a different time period.
Data Augmentation	Apply random transformations to increase the effective dataset size.
Bayesian Methods	Use Bayesian techniques for modeling uncertainty.
Domain Knowledge	Incorporate domain expertise into feature selection and model design.
Regularly Validate and Update Models	Continuously monitor and retrain models with new data or updated features.