Explain different types of Naïve Bayes Classifiers

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Reference no: EM133236905

Assignment - K Nearest Neighbors Algorithm Questions

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 - Data on Rate of Return of Investment in Securities

Reference no: EM133236905

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