We will use the randomForest() function to create the decision tree and see it's graph.
When we execute the above code, it produces the following result −
randomForest(formula = nativeSpeaker ~ age + shoeSize + score,
data = readingSkills)
Type of random forest: classification
Number of trees: 500
No. of variables tried at each split: 1
OOB estimate of error rate: 1%
no yes class.error
no 99 1 0.01
yes 1 99 0.01
From the random forest shown above we can conclude that the shoesize and score are the important factors deciding if someone is a native speaker or not.
Also the model has only 1% error which means we can predict with 99% accuracy.
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