Machine Learning Bookcamp

4 Evaluation metrics for classification

This chapter covers

• Accuracy as a way of evaluating binary classification models and its limitations
• Determining where our model makes mistakes using a confusion table
• Deriving other metrics like precision and recall from the confusion table
• Using ROC (receiver operating characteristics) and AUC (area under the ROC curve) to further understand the performance of a binary classification model
• Cross-validating a model to make sure it behaves optimally
• Tuning the parameters of a model to achieve the best predictive performance

In this chapter, we continue with the project we started in the previous chapter: churn prediction. We have already downloaded the dataset, performed the initial preprocessing and ...