1. Explain Naïve Bayes Classifier?

Answer: There are three naïve Bayes classifiers:

The Multinomial classifier uses multinomial distribution on each word of a sentence. Every word is treated independently rather than being treated as a part of the sentence.

The Gaussian classifier is utilized with continuous data. It assumes that each data class is distributed as Gaussian distribution.

The Bernoulli classifier assumes that every feature present is binary, which means it can only take either of the two values.

2. Explain the strength of Bayesian statistics?

Answer: It is sometimes preferred over other methods, here’s why:

Bayesian gives intuitive and direct inferences. It meaningfully tells the probability of a hypothesis being true. It enhances the power of answering complicated questions easily and clearly. Bayesian uses available information to find the probability. Based upon from data, the method uses prior information as well. The method enhances decision-making. When there is a lack of parameters and facts, Bayesian quantify uncertainties using available evidence.

3. Do you think that Bayesian statistics has the power to replace Frequentists?

Answer: Both frequentists and Bayesian statistics have specific applications, which is why these methods are used frequently. If you can solve a certain problem with both Bayesian and frequentists, use the one that does it simply.

For Example: when you have to solve huge problems that have streaming data, Bayesian will only give an approximation.

4. What are some unique applications of Bayesian Statistics and Bayes theorem?

Answer: There are various unique applications of it and Bayes theorem. Here are some of these:

1. It can be used to decide whether a project will finish on time or not? There are only two possible outcomes, either it will finish on/before time or it will not.

2. Using multiple blood samples to decide diseases.

3. Utilizing it as a spam filter considering previous patterns.

4. Helps to detect whether a certain water body is fit for various purposes such as drinking, agriculture, etc. Since, due to the presence of various pollutants, it is not possible to give an exact quantifier, the Bayesian method is used.

5. Explain Central Limit Theorem?

Answer: As the sample size increases, the sampling distribution of sample means approaches a normal distribution. possible random samples of size n are selected from a population with mean μ and standard deviation σ, the mean of the sample means is denoted by μ x̄ , so,

μ x̄ = μ

The standard deviation of the sample means is:  σ x̄ = σ⁄√ n

6. How can you generate a random number between 1 – 7 with only a die?

Answer: Lets see below.

1. Any die has six sides from 1-6. There is no possibility to get seven equal outcomes from a single rolling of a die. If we roll the die twice and consider the event of two rolls, we now have 36 different outcomes.

2. To get our 7 equal outcomes we have to reduce this 36 to a number divisible by 7. Thus we consider only 35 outcomes and exclude the other one.

3. A simple scenario can be to exclude the combination (6,6), i.e., to roll the die again if 6 appears twice.

4. All the remaining combinations from (1,1) till (6,5) can be divided into 7 parts of 5 each. In this way all the seven sets of outcomes are equally likely.

7. What is Collaborative filtering?

Answer: The process of filtering used by most of the recommender systems to find patterns or information by collaborating viewpoints, various data sources and multiple agents.