# Introduction In this lab, we will demonstrate how to measure the Out-Of-Bag (OOB) error rate for a Random Forest model using the Python scikit-learn library. The OOB error rate is the average error for each training observation calculated using predictions from the trees that do not contain the observation in their respective bootstrap sample. This allows the Random Forest model to be fit and validated while being trained. ## 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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