In this chapter, we learned how to evaluate the performance of a model as the average accuracy of the prediction. We understood how to determine an accurate cross-validation index expressing the accuracy. Starting from the cross-validation index, we tuned the parameters. In addition, we learned how to select the features using a filter or a frapper and how to tune features and parameters at the same time. This chapter described the last part of building a machine learning solution and the next chapter shows an overview of some of the most important machine learning techniques.