Regularization techniques are essential in mitigating the problem of overfitting in machine learning models. Overfitting occurs when a model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. This leads to poor performance on unseen data. Let's explore three commonly used regularization techniques:
L1 Regularization: L1 regularization, also known as Lasso regularization, adds a penalty term to the model's loss function that encourages sparsity in the feature weights. By penalizing large weights, L1 regularization helps in feature selection, effectively reducing the complexity of the model.
L2 Regularization: L2 regularization, also known as Ridge regularization, adds a penalty term proportional to the square of the magnitude of the weight coefficients to the model's loss function. This technique encourages smaller and more evenly distributed weights, preventing the model from relying too heavily on any single feature.
Dropout: Dropout is a technique where randomly selected neurons or units are ignored during training. By dropping out a fraction of neurons at each training iteration, dropout helps in reducing the model's reliance on specific features or combinations of features. This, in turn, improves the model's ability to generalize to unseen data.