Computing probabilities
The where9am
data frame contains 91 days (thirteen weeks) worth of data in which Brett recorded his location
at 9am each day as well as whether the daytype
was a weekend or weekday.
Using the conditional probability formula below, you can compute the probability that Brett is working in the office, given that it is a weekday.
$$ P(A|B) = \frac{P(A \text{ and } B)}{P(B)} $$
Calculations like these are the basis of the Naive Bayes destination prediction model you'll develop in later exercises.
This exercise is part of the course
Supervised Learning in R: Classification
Exercise instructions
- Find P(office) using
nrow()
andsubset()
to count rows in the dataset and save the result asp_A
. - Find P(weekday), using
nrow()
andsubset()
again, and save the result asp_B
. - Use
nrow()
andsubset()
a final time to find P(office and weekday). Save the result asp_AB
. - Compute P(office | weekday) and save the result as
p_A_given_B
. - Print the value of
p_A_given_B
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Compute P(A)
p_A <- ___
# Compute P(B)
p_B <- ___
# Compute the observed P(A and B)
p_AB <- ___
# Compute P(A | B) and print its value
p_A_given_B <- ___
___