Questions

1. Why is it important to encode categorical features?
2. What are the different ways to stop a decision tree calculation?
3. Temperature_hot has an entropy value of one in the example. Why?
4. Following the diagram of the decision tree at the beginning of the Understanding supervised learning with decision trees section, what would be the path to decide whether or not to train outside? Consider using IF statements.
5. Would the cluster distribution change if we choose different starting points? You can read about this in the recommended articles.
6. Is the clustering that's obtained with iterative analysis the same as the one that's determined visually? Why?