Regularization
Regularization is a technique used in machine learning to prevent overfitting.
Overfitting occurs when a model is too complex and fits the training data too closely,
- including the noise and random fluctuations in the data.
As a result, the model has poor generalization performance and performs poorly on unseen data.
Regularization adds a penalty term to the loss function during training
- to discourage the model from fitting the noise and irrelevant features.
- This term typically penalizes large weights in the model,
- encouraging the model to have smaller weight values and a simpler structure.
There are two main types of regularization:
L1 and L2 regularization.
L1 regularization adds a penalty term proportional to the absolute value of the weights,
while L2 regularization adds a penalty term proportional to the square of the weights.
Regularization is important in model training because
- it helps to prevent overfitting and
- improve the generalization performance of the model.
By encouraging the model to have a simpler structure
- and to fit the data in a more general way,
- regularization reduces the risk of the model memorizing the training data
- and improves its ability to make accurate predictions on new, unseen data