Mitigating Overfitting with Regularization

Requirements Clarification & Assessment

• Understanding Overfitting: Overfitting occurs when a machine learning model learns the training data too well, capturing noise and details that do not generalize to new data. This results in poor performance on unseen datasets.

• Role of Regularization: Regularization is designed to reduce the complexity of a model by discouraging it from fitting the noise in the training data, thereby improving its generalization capabilities.

• Types of Regularization:

  • L1 Regularization (Lasso): Adds a penalty equal to the absolute value of the magnitude of coefficients, encouraging sparsity in the model.
  • L2 Regularization (Ridge): Adds a penalty equal to the square of the magnitude of coefficients, shrinking them towards zero but not exactly zero.
  • Elastic Net: A combination of L1 and L2 regularization that balances between sparsity and shrinkage.

• Objective: Ensure the model remains simple enough to generalize well to new data while maintaining sufficient complexity to capture the underlying patterns in the training data.