Preparing for unforeseen circumstances
While Brett was tracking his location over 13 weeks, he never went into the office during the weekend. Consequently, the joint probability of P(office and weekend) = 0.
Explore how this impacts the predicted probability that Brett may go to work on the weekend in the future. Additionally, you can see how using the Laplace correction will allow a small chance for these types of unforeseen circumstances.
The model locmodel
is available for you to use, along with the data frame weekend_afternoon
. The naivebayes
package has also been pre-loaded.
This exercise is part of the course
Supervised Learning in R: Classification
Exercise instructions
- Use the
locmodel
to output predicted probabilities for a weekend afternoon by using thepredict()
function. Remember to set thetype
argument. - Create a new naive Bayes model with the Laplace smoothing parameter set to
1
. You can do this by setting thelaplace
argument in your call tonaive_bayes()
. Save this aslocmodel2
. - See how the new predicted probabilities compare by using the
predict()
function on your new model.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Observe the predicted probabilities for a weekend afternoon
# Build a new model using the Laplace correction
locmodel2 <- ___
# Observe the new predicted probabilities for a weekend afternoon