A more sophisticated location model
The locations
dataset records Brett's location every hour for 13 weeks. Each hour, the tracking information includes the daytype
(weekend or weekday) as well as the hourtype
(morning, afternoon, evening, or night).
Using this data, build a more sophisticated model to see how Brett's predicted location not only varies by the day of week but also by the time of day. The dataset locations
is already loaded in your workspace.
You can specify additional independent variables in your formula using the +
sign (e.g. y ~ x + b
).
The naivebayes
package has been pre-loaded.
This exercise is part of the course
Supervised Learning in R: Classification
Exercise instructions
- Use the R formula interface to build a model where location depends on both
daytype
andhourtype
. Recall that the functionnaive_bayes()
takes 2 arguments:formula
anddata
. - Predict Brett's location on a weekday afternoon using the data frame
weekday_afternoon
and thepredict()
function. - Do the same for a
weekday_evening
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Build a NB model of location
locmodel <- ___
# Predict Brett's location on a weekday afternoon
___
# Predict Brett's location on a weekday evening
___