Overfitting Prevention

Overfitting in machine learning happens when the model learns from the training data too well, including non-relevant details. This leads the model to perform very well on the training data but poorly on other data.

Prevention techniques:

• Regularization (L1/L2): This method adds a penalty to large weights to keep the model from becoming too complex.
• Cross-validation: This helps test the model on different slices of data to make sure it generalizes well.
• Pruning (for tree models): Cuts back unnecessary branches that overcomplicate the model.
• Early stopping: Stops training when performance stops improving on the validation set.
• Dropout (for neural nets): This method randomly drops neurons during training so the network doesn't become too dependent on specific paths.