Tutorial: Obtaining feature importance using variable importance plots

February 25, 2015

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A common question when building a predictive model is: "What features are important?". We show a simple way to get feature importance plot using randomForest package in R.
R package 'randomForest' allows users to obtain a plot of feature importance. In this tutorial, we have shown how to understand which features are important in predicting the target variable in both a classification and a regression scenario. # Related 1. [Tutorial: Creating a random forest regression model in R and using it for scoring](https://gallery.azureml.net/Details/b729c21014a34955b20fa94dc13390e5) 2. [Tutorial: Building a classification model in Azure ML](https://gallery.azureml.net/Details/01b2765fa75147ce99679e18482d280f) 3. [Tutorial: Base R Graphics in AzureML](https://gallery.azureml.net/Details/0a715b439b5c43b2aa104a92f215624a) 4. [Tutorial: Using R package ggplot2 in Azure ML. Histograms, density plots and violin plots](https://gallery.azureml.net/Details/b1c26728eb6c4e4d80dddceae992d653)