Experimental Design
Regularization techniques are methods used in statistical modeling and machine learning to prevent overfitting by adding a penalty to the model's complexity. These techniques help improve the model's performance on unseen data by discouraging overly complex models that fit the training data too closely. In the context of big data and high-dimensional experiments, regularization is essential for managing the challenges posed by a large number of variables relative to the number of observations.
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