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  3. Supervised Learning in R: Classification

Exercise

Seeing how the neighbors voted

When multiple nearest neighbors hold a vote, it can sometimes be useful to examine whether the voters were unanimous or widely separated.

For example, knowing more about the voters' confidence in the classification could allow an autonomous vehicle to use caution in the case there is any chance at all that a stop sign is ahead.

In this exercise, you will learn how to obtain the voting results from the knn() function.

The class package has already been loaded in your workspace along with the datasets signs, sign_types, and signs_test.

Instructions

100 XP
  • Build a kNN model with the prob = TRUE parameter to compute the vote proportions. Set k = 7.
  • Use the attr() function to obtain the vote proportions for the predicted class. These are stored in the attribute "prob".
  • Examine the first several vote outcomes and percentages using the head() function to see how the confidence varies from sign to sign.