I need an explanation for this C Programming question to help me study.

**Instructions**

This document was purposely created in Microsoft Word so you can enter your answers into the document.

**YOUR ANSWERS MUST APPEAR WITHIN THE PROBLEM DOCUMENT.**

**10% WILL BE DEDUCTED IF YOU CREATE A NEW OR SEPARATE DOCUMENT.**

**10% WILL BE DEDUCTED IF YOU CREATE A “TITLE PAGE” TYPE OF DOCUMENT.**

**20% WILL BE IF YOU DO NOT SHOW YOUR CALCULATIONS FOR EACH ANSWER.**

You must make your own calculations and you must show your calculations in the answer document.Insufficient calculation steps will result in reduced points earned.

Imbalanced Classifiers

1.Begin by writing the formula for each calculation, then show your steps to arrive at your answer.

a. Calculate Accuracy

b.Precision

c.Recall

d.F- Measure

2.Begin by writing the formula for each calculation, then show your steps to arrive at your answer.

a. Calculate Accuracy

b.Precision

c.Recall

d.F- Measure

Bayes Theorem

3. (a) Suppose the fraction of undergraduate students who smoke is 15% and

the fraction of graduate students who smoke is 23%. If one-fifth of the college students are graduate students and the rest are undergraduates, what is the probability that a student who smokes is a graduate student?

Answer

(b) Given the information in part (a), is a randomly chosen college student

more likely to be a graduate or undergraduate student?

Answer

(c) Repeat part (b) assuming that the student is a smoker.

Answer:

(d) Suppose 30% of the graduate students live in a dorm but only 10% of

the undergraduate students live in a dorm. If a student smokes and lives in the dorm, is he or she more likely to be a graduate or undergraduate student? You can assume independence between students who live in a dorm and those who smoke.

Answer:

Bayes Theorem

4.Consider the data set below.

a) Estimate the conditional probabilities for (P(A|+), P(B|+), P(C|+), P(A|-). P(B|-), P(C|-)

(b) Use the estimate of conditional probabilities given in the previous question to predict the class label for a test sample (A =0, B =1, C =0) using the naïve Bayes approach.