Counter Overfitting with L1 and L2 Regularization

Overfitting. A modeling error many of us have encountered or will encounter while training a model. Simply put, overfitting is when the model learns about the details and noise of a dataset to the extent that it negatively impacts the model on new data. An overfitted model will do well on the training dataset but perform poorly on unseen data, leading to poor performances on test and validation sets.

To counter this, researchers have …

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