# Introduction In this lab, we will compare the performance of two popular ensemble models, Random Forest (RF) and Histogram Gradient Boosting (HGBT), for a regression dataset in terms of score and computation time. We will vary the parameters that control the number of trees according to each estimator and plot the results to visualize the trade-off between elapsed computing time and mean test score. ## 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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