Databricks Machine Learning (ML) Associate Practice Test

The primary goal of regularization techniques in machine learning is to prevent overfitting. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise, leading to poor performance on unseen data. Regularization introduces additional information or constraints into the model, effectively adding a penalty for complexity. This encourages the model to focus on the most significant features and leads to simpler, more generalized solutions that perform better during validation and testing phases.

While some techniques may also simplify the model by reducing the number of parameters or features considered, the central objective is to enhance the model's ability to generalize by mitigating the adverse effects of overfitting. Regularization methods, such as L1 and L2 regularization, directly target this issue by modifying the loss function used during training, thus ensuring the model remains robust against variations in new data.