EXERCISES
CLARIFYING THE CONCEPTS
- With what information does Bayes Theorem update our previous knowledge about the data parameters?
- What does the prior probability represent?
- What formula represents how the data behave within the target variable's class values?
- What formula represents how the data behave without reference to the class values?
- What is the formula from the previous exercise called?
- What does the posterior probability represent?
- What do we use for a prior probability if we have no prior knowledge about the parameters?
- How does the maximum a posteriori hypothesis help us to classify a record?
- What is the class conditional independence assumption?
- If we have more than one predictor, how do we write p(X* ∣ Y = y*) if we have two predictor variables X* = {X1 = x1, X2 = x2}?
WORKING WITH THE DATA
For the following exercises, work with the wine_flag_training and wine_flag_test data sets. Use either Python or R to solve each problem.
- Create two contingency...