tut14

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Last edited Feb 13, 2020

Created on Feb 13, 2020

This example visualizes the relative importance of different variables in predicting income levels using a random forest model. The code loads an income dataset from GitHub, shuffles rows, and handles missing factor values before training the model with `randomForest`. It then relies on `ggRandomForests` to plot variable importance scores, showing which predictors most strongly influence income classification. The visual output highlights the ranked contribution of each feature, with the model trained on the processed data frame.

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