# Introduction Hyperparameters are parameters that are not directly learned by an estimator. They are passed as arguments to the constructor of the estimator classes. Tuning the hyperparameters of an estimator is an important step in building effective machine learning models. It involves finding the optimal combination of hyperparameters that result in the best performance of the model. Scikit-learn provides several tools to search for the best hyperparameters: `GridSearchCV` and `RandomizedSearchCV`. In this lab, we will walk through the process of tuning hyperparameters using these tools. ## 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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