Question

Data Science

Why do data analysts use regularization? Provide examples of regularizations and what they aim to achieve. No more than 10 lines.

Answer 1

Answer:-

Why do data analysts use regularization?

In general, regularization means to make things regular or acceptable. This is exactly why we use it for applied machine learning. In the context of machine learning, regularization is the process which regularizes or shrinks the coefficients towards zero. In simple words, regularization discourages learning a more complex or flexible model, to prevent overfitting.

The basic idea is to penalize the complex models i.e. adding a complexity term that would give a bigger loss for complex models.

examples:-

Regularization helps to solve over fitting problem in machine learning. Simple model will be a very poor generalization of data. At the same time, complex model may not perform well in test data due to over fitting. We need to choose the right model in between simple and complex model. Regularization helps to choose preferred model complexity, so that model is better at predicting. Regularization is nothing but adding a penalty term to the objective function and control the model complexity using that penalty term. It can be used for many machine learning algorithms.