Regularization
Regularization is a technique used in machine learning to prevent overfitting by adding a penalty to the loss function based on the complexity of the model. This penalty discourages the model from assigning excessive importance to any single feature or creating overly complex decision boundaries. Common types include L1 (Lasso) and L2 (Ridge) regularization, which aim to keep model weights small.
It's like a coach who encourages players to work together and not rely too heavily on a single star player, leading to a more balanced and robust team.
It's essential for building models that generalize well from training data to real-world applications, ensuring reliable performance.
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