Welcome to Part 10 - Model Selection & Boosting!

After we built our Machine Learning models, some questions remained unanswered:

1. How to deal with the bias variance tradeoff when building a model and evaluating its performance ?
2. How to choose the optimal values for the hyperparameters (the parameters that are not learned) ?
3. How to find the most appropriate Machine Learning model for my business problem ?

In this part we will answer these questions thanks to Model Selection techniques including:

1. k-Fold Cross Validation
2. Grid Search

Eventually we will finish this course by a last bonus section included in this part, dedicated to one of the most powerful Machine Learning model, that has become more and more popular: XGBoost.

Enjoy Machine Learning!