Hello my friends,

Here are some bonus questions and their answers that are often asked by the Data Science community.

1. What are the pros and cons of each Regression model?

Please find in the attached folder at the bottom of this article a cheat-sheet that gives you all the pros and the cons of each Regression model.

2. How can I improve each Regression models?

In Part 10 - Model Selection, you will find the second section dedicated to Parameter Tuning, that will allow you to improve the performance of your models, by tuning them. You probably already noticed that each model is composed of two types of parameters:

• the parameters that are learnt, for example the coefficients in Linear Regression,

• the hyperparameters.

The hyperparameters are the parameters that are not learnt and that are fixed values inside the model equations. For example, the regularization parameter lambda or the inverse regularization parameter C are hyperparameters. So far we used the default value of these hyperparameters, and we haven't searched for their optimal value so that your model reaches even higher performance. Finding their optimal value is exactly what Parameter Tuning is about. So for those of you already interested in improving your model performance and doing some parameter tuning, feel free to jump directly to Part 10 - Model Selection.

And as an extra BONUS, please find in the attached folder at the bottom of this article some slides we made about Regularization.

Now congratulations for having completed Part 2, and let's move on to the next part of the journey:

Part 3 - Classification