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The correct answer is C. Bayesian classifier.<\/p>\n
A decision tree is a supervised learning model that uses a tree-like structure to make predictions. The tree is built by splitting the data into smaller and smaller subsets until each subset contains only one class. The leaves of the tree are the class labels, and the branches represent the features that were used to make the predictions.<\/p>\n
CART (Classification and Regression Trees) is a decision tree algorithm that is often used for classification tasks. It is a greedy algorithm, which means that it chooses the best split at each node in the tree, without considering the consequences of its choices on later nodes.<\/p>\n
ID3 (Iterative Dichotomiser 3) is another decision tree algorithm that is often used for classification tasks. It is a top-down algorithm, which means that it starts with the entire dataset and then splits it into smaller and smaller subsets until each subset contains only one class.<\/p>\n
A Bayesian classifier is a probabilistic classifier that uses Bayes’ theorem to make predictions. It is a non-parametric classifier, which means that it does not make any assumptions about the distribution of the data.<\/p>\n
A random forest is a machine learning algorithm that uses a collection of decision trees to make predictions. The trees are trained on different subsets of the data, and the predictions of the trees are then combined to make a final prediction.<\/p>\n
Bayesian classifiers are not tree-based learners. They are probabilistic classifiers that use Bayes’ theorem to make predictions.<\/p>\n","protected":false},"excerpt":{"rendered":"
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