- 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 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.