Testing other 'k' values
By default, the knn()
function in the class
package uses only the single nearest neighbor.
Setting a k
parameter allows the algorithm to consider additional nearby neighbors. This enlarges the collection of neighbors which will vote on the predicted class.
Compare k
values of 1, 7, and 15 to examine the impact on traffic sign classification accuracy.
The class
package is already loaded in your workspace along with the datasets signs
, signs_test
, and sign_types
. The object signs_actual
holds the true values of the signs.
This exercise is part of the course
Supervised Learning in R: Classification
Exercise instructions
- Compute the accuracy of the default
k = 1
model using the given code, then find the accuracy of the model usingmean()
to comparesigns_actual
and the model's predictions. - Modify the
knn()
function call by settingk = 7
and again find accuracy value. - Revise the code once more by setting
k = 15
, plus find the accuracy value one more time.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Compute the accuracy of the baseline model (default k = 1)
k_1 <- knn(train = ___, test = ___, cl = ___)
mean(___)
# Modify the above to set k = 7
k_7 <- ___
mean(___)
# Set k = 15 and compare to the above
k_15 <- ___
mean(___)