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Building a random forest model

In spite of the fact that a forest can contain hundreds of trees, growing a decision tree forest is perhaps even easier than creating a single highly-tuned tree.

Using the randomForest package, build a random forest and see how it compares to the single trees you built previously.

The loans_train and loans_test datasets are pre-loaded for you.

Keep in mind that due to the random nature of the forest, the results may vary slightly each time you create the forest.

说明

100 XP
  • Load the randomForest package.
  • Build a random forest model using all of the loan application variables. The randomForest function also uses the formula interface.
  • Compute the accuracy of the random forest model to compare to the original tree's accuracy of 57.6% using predict() and mean().