Regularization is a technique that prevents overfitting in statistical analysis. Overfitting occurs when a statistical model learns the training data too well and is unable to generalize to new data. Regularization works by penalizing the model for being too complex. This forces the model to learn only the most important features of the data, which makes it more likely to generalize well to new data. There are many different regularization techniques, some are
- L1 regularization: L1 regularization penalizes the model for the sum of the absolute values of its coefficients. This forces the model to learn only the most important features of the data, because it will be penalized for including too many features.
- L2 regularization: L2 regularization penalizes the model for the sum of the squared values of its coefficients. This forces the model to learn smaller coefficients, which makes it less likely to overfit the data.