tut10

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Last edited Feb 13, 2020

Created on Feb 13, 2020

This example shows the results of a Boruta feature-selection analysis on a Brexit-related dataset, displaying the importance of each predictor variable in relation to the target variable, Percent.Leave. The visualization uses the Boruta R package to run the algorithm, which compares each feature's importance against shadow attributes to determine if it is important, unimportant, or a tentative holdout. The code calls the `Boruta()` function with the formula and data, then prints the output object to reveal the decision for each attribute. The rendered result is a textual summary of the Boruta run, highlighting which variables are confirmed as significant for the model.

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