Regularization Vs Standardization

Regularization

• Regularization is a technique to discourage the complexity of the model.
• It does this by penalizing the loss function.
• This helps to solve the over-fitting problem. 
• An over-fitted model performs well on training data but fails to generalize on test data.

Standardization

• Standardization is the process of re-scaling data to have a mean value of 0 and a standard deviation of 1.
• Often, raw data is comprised of attributes with varying scales.
• For example, one attribute may be in kilograms and another may be a count.
• Although not required, you can often get a boost in performance by re-scaling your data.