The maximum likelihood method can be used to explore relationships among more diverse sequences, conditions that are not well handled by maximum parsimony methods.

Related MCQs

• 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
• Select the correct answers for following statements. 1. Filter methods are much faster compared to wrapper methods. 2. Wrapper methods use statistical methods for evaluation of a subset of features while Filter methods use cross validation.
• The main disadvantage of maximum likelihood methods is that they are _____
• In Naive Bayes equation P(C / X)= (P(X / C) *P(C) ) / P(X) which part considers "likelihood"?
• 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
• Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance
• Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance
• Suppose on performing reduced error pruning, we collapsed a node and observed an improvement in the prediction accuracy on the validation set. Which among the following statements are possible in light of the performance improvement observed? (a) The collapsed node helped overcome the effect of one or more noise affected data points in the training set (b) The validation set had one or more noise affected data points in the region corresponding to the collapsed node (c) The validation set did not have any data points along at least one of the collapsed branches (d) The validation set did have data points adversely affected by the collapsed node
• Wrapper methods are hyper-parameter selection methods that
• Which of the following methods/methods do we use to find the best fit line for data in Linear Regression?