Regularization is a technique used in machine learning to prevent overfitting of the model to the training data. It involves adding a penalty for the model's complexity to the loss function, forcing the model to simplify.
Main regularization methods:
- L1 Regularization (Lasso): Adds a penalty equal to the sum of the absolute values of the model's weights.
from sklearn.linear_model import Lasso model = Lasso(alpha=0.1)
- L2 Regularization (Ridge): Adds a penalty equal to the sum of the squares of the model's weights.
from sklearn.linear_model import Ridge model = Ridge(alpha=0.1)
- Elastic Net: A combination of L1 and L2 regularization.
from sklearn.linear_model import ElasticNet model = ElasticNet(alpha=0.1, l1_ratio=0.5)
- Dropout: A technique used in neural networks, randomly dropping neurons during training.
from tensorflow.keras.layers import Dropout model.add(Dropout(0.5))
- Early Stopping: Stopping the training of the model when its performance on validation data stops improving.
Advantages of regularization:
- Prevents overfitting.
- Improves the model's generalization ability.
- Simplifies the model.
Regularization is a crucial component in the machine learning process, allowing for the creation of more efficient and general models.
