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

View Course

Exercise instructions

Use the locmodel to output predicted probabilities for a weekend afternoon by using the predict() function. Remember to set the type argument.
Create a new naive Bayes model with the Laplace smoothing parameter set to 1. You can do this by setting the laplace argument in your call to naive_bayes(). Save this as locmodel2.
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

Edit and Run Code