21. Let's say, a "Linear regression" model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?
22. SVM is a learning
23. Which of the following quantities are minimized directly or indirectly during parameter estimation in Gaussian distribution Model?
24. What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach?
25. If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is
26. Below are the two ensemble models: 1. E1(M1, M2, M3) and 2. E2(M4, M5, M6) Above, Mx is the individual base models. Which of the following are more likely to choose if following conditions for E1 and E2 are given? E1: Individual Models accuracies are high but models are of the same type or in another term less diverse E2: Individual Models accuracies are high but they are of different types in another term high diverse in nature
27. Naive Bayes classifiers are a collection . . . . . . . . of algorithms
28. The K-means algorithm:
29. 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
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
30. 100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, what is the probability of being a man
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