# Introduction Nested cross-validation is a technique used to estimate the generalization error of a model and its hyperparameters. It is especially useful when choosing between different models or when hyperparameters need to be optimized. In this tutorial, we will compare non-nested and nested cross-validation on a support vector classifier model using the iris dataset. We will also visualize the difference in performance between the two methods. ## 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.
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