Introduction
In this lab, we will learn how to use Decision Trees for classification using scikit-learn. Decision Trees are a non-parametric supervised learning method used for classification and regression. They are simple to understand and interpret, and can handle both numerical and categorical data.
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.
Skills Graph
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flowchart RL
sklearn(("`Sklearn`")) -.-> sklearn/ModelSelectionandEvaluationGroup(["`Model Selection and Evaluation`"])
sklearn(("`Sklearn`")) -.-> sklearn/UtilitiesandDatasetsGroup(["`Utilities and Datasets`"])
ml(("`Machine Learning`")) -.-> ml/FrameworkandSoftwareGroup(["`Framework and Software`"])
sklearn/ModelSelectionandEvaluationGroup -.-> sklearn/metrics("`Metrics`")
sklearn/UtilitiesandDatasetsGroup -.-> sklearn/datasets("`Datasets`")
sklearn/ModelSelectionandEvaluationGroup -.-> sklearn/model_selection("`Model Selection`")
ml/FrameworkandSoftwareGroup -.-> ml/sklearn("`scikit-learn`")
subgraph Lab Skills
sklearn/metrics -.-> lab-71107{{"`Decision Tree Classification with Scikit-Learn`"}}
sklearn/datasets -.-> lab-71107{{"`Decision Tree Classification with Scikit-Learn`"}}
sklearn/model_selection -.-> lab-71107{{"`Decision Tree Classification with Scikit-Learn`"}}
ml/sklearn -.-> lab-71107{{"`Decision Tree Classification with Scikit-Learn`"}}
end