In machine learning, Regularization is a process of introducing additional information in order to avoid overfitting.
Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. This results in the model fitting the training data too closely, and in turn, generalizing poorly to new data (test data).
Regularization is a technique that can be used to combat overfitting. It involves adding a penalty term to the objective function that is used to train the model. This penalty term encourages the model to be simpler and prevents it from overfitting the training data.
There are two main types of regularization:
L1 regularization: This adds a penalty term to the objective function that is proportional to the absolute value of the weights. This encourages the model to use only a few of the features and to set the weights of the unused features to 0.
L2 regularization: This adds a penalty term to the objective function that is proportional to the square of the weights. This encourages the model to spread the weights evenly among the features and to avoid setting the weights of the unused features to 0.
L1 regularization is often used in conjunction with L2 regularization. This is because L1 regularization can lead to sparse models, which are models that have a large number of weights that are set to 0. L2 regularization, on the other hand, tends to produce models that are denser, with fewer weights set to 0.
The trade-off between L1 and L2 regularization is that L1 regularization can lead to better performance on the training data but poorer performance on the test data. L2 regularization, on the other hand, can lead to poorer performance on the training data but better performance on the test data.
It is important to tune the regularization parameter in order to achieve the best results.
Regularization is a powerful technique that can be used to improve the performance of machine learning models. It is important to tune the regularization parameter in order to achieve the best results.
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