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Understanding Regularization for Logistic Regression | KNIME

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Learn about regularization for logistic regression and when to use L1, L2, Gauss, and Laplace.

Regularization can be used to avoid overfitting. But what actually is regularization, what are the common techniques, and how do they differ? Regularization is any modification we make to a learning algorithm that is intended to reduce its generalization error but not its training error. Ian Goodfellow Regularization can be used to train models that generalize better on unseen data, by preventing the algorithm from overfitting the training dataset. So how can we modify the logistic regression algorithm to reduce the generalization error? Common approaches I found are Gauss, Laplace, L1 and L2.

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