# Introduction In this tutorial, we will compare Kernel Ridge Regression (KRR) and Support Vector Regression (SVR) using Scikit-Learn, a popular machine learning library in Python. Both models learn a non-linear function by employing the kernel trick. KRR and SVR differ in their loss functions and fitting methods. We will use an artificial dataset consisting of a sinusoidal target function and strong noise added to every fifth datapoint. ## 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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