# Introduction This lab uses a synthetic dataset to compare two different Bayesian regressors: Automatic Relevance Determination and Bayesian Ridge Regression. The first part of the lab compares the models' coefficients with respect to the true coefficients by using an Ordinary Least Squares (OLS) model as a baseline. In the last section, the lab plots predictions and uncertainties for the ARD and the Bayesian Ridge regressions using a polynomial feature expansion to fit a non-linear relationship between `X` and `y`. ## 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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