gs gs126 Naive-Bayes Algorithm - Naive-Bayes Algorithm - Quiz No.1
gs gs126 Neural Networks Quiz
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Question 2: What is the formula for Bayes’ theorem? Where (A & B) and (H & E) are events and P(B), P(H) & P(E) ≠ 0.
P(H|E) = [P(E|H) * P(E)] / P(H)
P(A|B) = [P(A|B) * P(A)] / P(B)
P(H|E) = [P(H|E) * P(H)] / P(E)
P(A|B) = [P(B|A) * P(A)] / P(B)
Question 3: Which of the following statement is not true about Naïve Bayes classifier algorithm?
It cannot be used for Binary as well as multi-class classifications
It is the most popular choice for text classification problems
It performs well in Multi-class prediction as compared to other algorithms
It is one of the fast and easy machine learning algorithms to predict a class of test datasets
Question 4: What is the assumptions of Naïve Bayesian classifier?
It assumes that features of a data are completely dependent on each other
It assumes that each input variable is dependent and the model is not generative
It assumes that each input attributes are independent of each other and the model is generative
It assumes that the data dimensions are dependent and the model is generative
Question 5: Which of the following is not a supervised machine learning algorithm?
Decision tree
SVM for classification problems
Naïve Bayes
K-means
Question 6: Which one of the following terms is not used in the Bayes’ Theorem?
Prior
Unlikelihood
Posterior
Evidence
Question 8: In which of the following case the Naïve Bayes’ algorithm does not work well?
When faster prediction is required
When the Naïve assumption holds true
When there is the case of Zero Frequency
When there is a multiclass prediction
Question 10: Which one of the following models is a generative model used in machine learning?
Linear Regression
Logistic Regression
Naïve Bayes
Support vector machines
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