Exercises

Answering the following questions is of extreme importance: you are building your ML foundations—do not skip this step!

1. Given a dataset of 1,000 labeled examples, what do you have to do if you want to measure the performance of a supervised learning algorithm during the training, validation, and test phases, while using accuracy as the unique metric?
2. What is the difference between supervised and unsupervised learning?
3. What is the difference between precision and recall?
4. A model in a high-recall regime produces more or less false positives than a model in a low recall regime?
5. Can the confusion matrix only be used in a binary classification problem? If not, how can we use it in a multiclass classification problem?
6. Is one-class classification a supervised learning problem? If yes, why? If no, why?
7. If a binary classifier has an AUC of 0.5, what can you conclude from...