Features being classified is independent of each other in Naïve Bayes Classifier

Related MCQs

• Features being classified is independent of each other in Na�ve Bayes Classifier
• Features being classified is                    of each other in Na�ve Bayes Classifier
• Features being classified is __________ of each other in Naïve Bayes Classifier
• 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
• 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
• What is the naïve assumption in a Naïve Bayes Classifier.
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• Multinomial Na�ve Bayes Classifier is                     distribution
• Gaussian Na�ve Bayes Classifier is                     distribution
• Multinomial Naïve Bayes Classifier is     _                distribution