Examining "raw" probabilities

The naivebayes package offers several ways to peek inside a Naive Bayes model.

Typing the name of the model object provides the a priori (overall) and conditional probabilities of each of the model's predictors. If one were so inclined, you might use these for calculating posterior (predicted) probabilities by hand.

Alternatively, R will compute the posterior probabilities for you if the type = "prob" parameter is supplied to the predict() function.

Using these methods, examine how the model's predicted 9am location probability varies from day-to-day. The model locmodel that you fit in the previous exercise is available for you to use, and the naivebayes package has been pre-loaded.

Deze oefening maakt deel uit van de cursus

Supervised Learning in R: Classification

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Oefeninstructies

Print the locmodel object to the console to view the computed a priori and conditional probabilities.
Use the predict() function similarly to the previous exercise, but with type = "prob" to see the predicted probabilities for Thursday at 9am.
Compare these to the predicted probabilities for Saturday at 9am.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Examine the location prediction model


# Obtain the predicted probabilities for Thursday at 9am
predict(___, ___ , type = ___)

# Obtain the predicted probabilities for Saturday at 9am

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