100 Facts About Regularization Techniques

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Here’s a list of 100 facts about regularization techniques in machine learning:

1. Regularization is a set of techniques used to prevent overfitting in machine learning models.
2. Overfitting occurs when a model performs well on the training data but fails to generalize to new, unseen data.
3. L1 regularization adds the sum of absolute values of coefficients to the cost function.
4. L2 regularization adds the sum of squared values of coefficients to the cost function.
5. Elastic Net regularization combines L1 and L2 regularization.
6. Regularization penalties help control the magnitude of coefficients in a model.
7. Ridge regression is another name for L2 regularization.
8. Lasso regression is another name for L1 regularization.
9. Regularization terms are hyperparameters that control the strength of regularization.
10. The regularization parameter is often denoted as alpha (α) in mathematical formulations.
11. Regularization encourages the model to find a balance between fitting the training data and avoiding excessive complexity.
12. Regularization is essential when dealing with high-dimensional data or datasets with many features.