Naive Bayes classifiers is                              Learning

Related MCQs

• Naive Bayes classifiers is _______________ Learning
• Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other
• In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea
• Naive Bayes classifiers are a collection ------------------of algorithms�
• Naive Bayes classifiers are a collection ------------------of algorithms 
• What is the naïve assumption in a Naïve Bayes Classifier.
• What is true about an ensembled classifier? 1. Classifiers that are more “sure” can vote with more conviction 2. Classifiers can be more “sure” about a particular part of the space 3. Most of the times, it performs better than a single classifier
• Which of the following statements about Naive Bayes is incorrect?
• In Naive Bayes equation P(C / X)= (P(X / C) *P(C) ) / P(X) which part considers "likelihood"?
• Which of the following is true about Naive Bayes ?