Problem
I. In as much detail as possible, describe how the K-Nearest Neighbor Algorithm will classify a new observation given some dataset. Go into detail about scaling features as needed.
II. Given two events A and B, describe what means in general terms. Give the formula if events A and B are independent of each other. Give its formula if events A and B are not independent of each other. Give examples of real word events that fit each situation.
III. Given a dataset, in as much detail as possible, describe how a decision tree will classify a new observation based off of that dataset. What exactly is a random forest?
IV. In as much detail as possible, describe how classification rules will classify a new observation, and then describe the similarities and differences between classification rules and Decision Trees.
V. In as much detail as possible, describe how the K-Means Clustering Algorithms works. Then tell the differences and similarities between K-Nearest Neighbors algorithm and K-Means Clustering.