# Introduction In machine learning, hyperparameters are parameters that are not learned from data, but rather set prior to training. Selecting appropriate hyperparameters is crucial to achieving high accuracy in machine learning models. Two common methods for hyperparameter optimization are randomized search and grid search. In this lab, we will compare these two methods for optimizing hyperparameters of a linear Support Vector Machine (SVM) with Stochastic Gradient Descent (SGD) training. ## 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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