# Introduction In this lab, we will use scikit-learn to demonstrate how to compute the positive and negative likelihood ratios (`LR+`, `LR-`) to assess the predictive power of a binary classifier. These metrics are independent of the proportion between classes in the test set, which makes them very useful when the available data for a study has a different class proportion than the target application. We will go through the following steps: ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
Click the virtual machine below to start practicing