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!
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