At the beginning of this chapter, we discussed how to chain different transformation techniques and classifiers in convenient model pipelines that helped us train and evaluate machine learning models more efficiently. We then used those pipelines to perform k-fold cross-validation, one of the essential techniques for model selection and evaluation. Using k-fold cross-validation, we plotted learning and validation curves to diagnose the common problems of learning algorithms, such as overfitting and underfitting. Using grid search, we further fine-tuned our model. We concluded this chapter by looking at a confusion matrix and various performance metrics that can be useful to further optimize a model's performance for a specific problem ...