Regularization vs Cross Validation: What is Difference?
While both regularization and cross validation are used in the process of building machine learning models, they serve very different purposes. Here’s a detailed comparison:
1. Definitions
- Regularization:
- Purpose: A set of techniques aimed at preventing overfitting by constraining the complexity of a model.
- How It Works: It adds a penalty term to the loss function (e.g., L1, L2 penalties) so that large parameter values are discouraged, leading to simpler models that generalize better.
- Cross Validation:
- Purpose: A model evaluation strategy used to assess how well a model generalizes to unseen data.
- How It Works: The dataset is partitioned into several subsets (folds). The model is trained on a subset of the data and validated on the remaining portion, and this process is repeated multiple times to obtain a robust estimate of model performance.
2. Key Differences
| Aspect | Regularization | Cross Validation |
|---|---|---|
| Objective | Prevent overfitting by controlling model complexity. | Estimate model performance and ensure generalizability. |
| Stage in Workflow | Applied during model training by modifying the loss function. | Used during model evaluation and hyperparameter tuning. |
| Methodology | Adds penalties (e.g., L1, L2) to the loss function. | Splits data into training and validation sets (e.g., k-fold CV). |
| Focus | Improves model robustness by discouraging extreme parameter values. | Provides insight into how the model will perform on unseen data. |
| Outcome | Results in a model that is less likely to overfit the training data. | Produces performance metrics (accuracy, RMSE, etc.) that inform model selection and tuning. |
3. How They Work Together
- Regularization directly modifies the training process to ensure that the model does not become too complex.
- Cross Validation is used to evaluate the effectiveness of the model (including the impact of regularization) by testing its performance on different subsets of data.
- In practice, you might use cross validation to select the best regularization parameter (e.g., choosing the optimal lambda in L2 regularization) by comparing performance across folds.
4. Final Thoughts
- Regularization is a technique to control model complexity during training, making your model simpler and more robust.
- Cross Validation is a model evaluation method that helps ensure that your model’s performance is reliable and generalizable.
They complement each other in the model development process—regularization improves model robustness, and cross validation helps you verify that robustness on unseen data.
Let me know if you need further clarification or additional details!