Overfitting 편집하기 (부분)

==Techniques to Prevent Overfitting==
Various methods are available to reduce or prevent overfitting in machine learning:
*'''Cross-Validation''': Techniques like k-fold cross-validation help estimate model performance on unseen data, providing a more reliable performance measure.
*'''Regularization''': Adds a penalty for large coefficients in models, such as L1 (Lasso) or L2 (Ridge) regularization, to simplify the model.
*'''Pruning (for Decision Trees)''': Limits the depth of a decision tree to prevent it from learning overly complex patterns.
*'''Dropout (for Neural Networks)''': Randomly removes neurons during each training iteration, preventing the network from relying too heavily on specific neurons.
*'''Early Stopping''': Stops training when performance on validation data starts to decline, preventing the model from learning irrelevant details.
*'''Data Augmentation''': Expands the training dataset by creating slightly modified copies of data, improving generalization by exposing the model to more variation.