Introduction
In this lab, we will use random forest to evaluate the importance of features on an artificial classification task. We will generate a synthetic dataset with only 3 informative features. The feature importances of the forest will be plotted, along with their inter-trees variability represented by the error bars.
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/UtilitiesandDatasetsGroup(["`Utilities and Datasets`"])
sklearn(("`Sklearn`")) -.-> sklearn/CoreModelsandAlgorithmsGroup(["`Core Models and Algorithms`"])
sklearn(("`Sklearn`")) -.-> sklearn/ModelSelectionandEvaluationGroup(["`Model Selection and Evaluation`"])
ml(("`Machine Learning`")) -.-> ml/FrameworkandSoftwareGroup(["`Framework and Software`"])
sklearn/UtilitiesandDatasetsGroup -.-> sklearn/datasets("`Datasets`")
sklearn/CoreModelsandAlgorithmsGroup -.-> sklearn/ensemble("`Ensemble Methods`")
sklearn/ModelSelectionandEvaluationGroup -.-> sklearn/inspection("`Inspection`")
sklearn/ModelSelectionandEvaluationGroup -.-> sklearn/model_selection("`Model Selection`")
ml/FrameworkandSoftwareGroup -.-> ml/sklearn("`scikit-learn`")
subgraph Lab Skills
sklearn/datasets -.-> lab-49132{{"`Feature Importance with Random Forest`"}}
sklearn/ensemble -.-> lab-49132{{"`Feature Importance with Random Forest`"}}
sklearn/inspection -.-> lab-49132{{"`Feature Importance with Random Forest`"}}
sklearn/model_selection -.-> lab-49132{{"`Feature Importance with Random Forest`"}}
ml/sklearn -.-> lab-49132{{"`Feature Importance with Random Forest`"}}
end