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.

Deze oefening maakt deel uit van de cursus

Supervised Learning in R: Classification

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Oefeninstructies

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.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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

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