# Introduction In machine learning, it is important to evaluate the quality of the predictions made by a model. This helps us understand how well the model is performing and whether it can be trusted for making accurate predictions. The scikit-learn library provides several metrics and scoring methods to quantify the quality of predictions. In this lab, we will explore three different APIs provided by scikit-learn for model evaluation: the Estimator score method, the scoring parameter, and the metric functions. ## 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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