Exercise

Visualizing model complexity

Now you have calculated the accuracy of the KNN model on the training and test sets using various values of n_neighbors, you can create a model complexity curve to visualize how performance changes as the model becomes less complex!

The variables neighbors, train_accuracies, and test_accuracies, which you generated in the previous exercise, have all been preloaded for you. You will plot the results to aid in finding the optimal number of neighbors for your model.

Instructions

100 XP
  • Add a title "KNN: Varying Number of Neighbors".
  • Plot the .values() method of train_accuracies on the y-axis against neighbors on the x-axis, with a label of "Training Accuracy".
  • Plot the .values() method of test_accuracies on the y-axis against neighbors on the x-axis, with a label of "Testing Accuracy".
  • Display the plot.