Regularization is a technique used in machine learning to prevent overfitting and improve the generalization of a model. Overfitting occurs when a model performs very well on the training data but poorly on unseen, or test, data. This happens when the model captures noise and random fluctuations in the training data rather than the underlying patterns that generalize to new data. Regularization methods add a penalty term to the model’s loss function, discouraging it from fitting the training data too closely or from using overly complex parameter values. Apart from it by obtaining a Machine Learning Certification, you can advance your career in Machine Learning. With this course, you can demonstrate your expertise in designing and implementing a model building, creating AI and machine learning solutions, performing feature engineering, many more fundamental concepts, and many more critical concepts among others.

**There are various forms of regularization, but two common ones are:**

**1.L1 Regularization (Lasso):** L1 regularization adds a penalty term proportional to the absolute values of the model’s coefficients (weights). It encourages the model to reduce the magnitude of some coefficients to zero, effectively performing feature selection by eliminating less important features. Lasso regularization is useful when you suspect that only a subset of features is relevant to the problem.

The L1 regularization term is added to the loss function as follows:

Loss_with_L1 = Loss_without_regularization + λ * Σ|w_i|,

where:

– λ (lambda) is the regularization strength, controlling the trade-off between fitting the data and regularization.

– w_i represents the model’s weights or coefficients.

**2. L2 Regularization (Ridge):** L2 regularization adds a penalty term proportional to the square of the model’s coefficients. It discourages the model from having very large weights, which can help in preventing overfitting and making the model more robust to small variations in the input data.

The L2 regularization term is added to the loss function as follows:

Loss_with_L2 = Loss_without_regularization + λ * Σ(w_i^2),

**where:**

– λ (lambda) is the regularization strength.

– w_i represents the model’s weights or coefficients.

The choice between L1 and L2 regularization, as well as the value of λ, is a hyperparameter that can be tuned through techniques like cross-validation to find the optimal balance between model complexity and generalization performance.

Regularization is a powerful tool in preventing overfitting and building more robust machine learning models, especially when dealing with high-dimensional data or limited data samples. It encourages models to be simpler and avoid fitting noise in the training data, leading to better performance on unseen data.