Part 1: (300-400 words)Explain the k-nearest neighbors algorithm for prediction, accompanied by a simple example.

Part 2: (300-400 words)

Naïve Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in

a wide variety of classification tasks.

1.

Please explain different types of Naïve Bayes Classifiers.

2.

Discuss advantages and disadvantages of Naive Bayes.

3.

Explain few applications of Naive Bayes.

Part 3: (1 page)

What makes a function of a discrete variable a candidate for a discrete random variable

distribution? What about the counterpart of this candidacy in the case of a continuous variable?

Explain in a one-page, APA formatted response.

Part 4:

For each investment class in Table 3, assume that future returns are normally distributed with the

population mean and standard deviation as given. Based on this assumption:

1. For each investment class, find the probability of a return that is less than zero (that is,

find the probability of a loss). Is your answer reasonable for all investment classes?

Explain.

2. For each investment class, find the probability of a return that is:

a. Greater than 5%.

b. Greater than 10%.

c. Greater than 20%.

d. Greater than 50%.

3. For which investment classes is the probability of the return greater than 50% is

essentially zero? For which investment classes is the probability of such a return greater

than 1 percent? Greater than 5%?

4. For which investment classes is the probability of loss essentially zero?

5. For which investment classes is the probability of loss greater than 1%? Greater than

10%? Greater than 20%?

Table 3

Data on Rate of Return of Investment in Securities

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