# Introduction In this lab, we will explore Gaussian Process Classification (GPC) with an RBF kernel and different choices of hyperparameters. We will generate data, train the GPC model with both fixed and optimized hyperparameters, and plot the posteriors and the log-marginal-likelihood landscape. We will also evaluate the accuracy and log-loss of the model. ## 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