Naive bayes classifier,random forest,decision tree classifier in machine learning mcq -01.02.24

Which of the following machine learning algorithms is based on the principle of Bagging?

Which classifier applies Bayes’ theorem with the 'naive' assumption of conditional independence between every pair of a given set of features?

Which of the following machine learning algorithms is based on the law of conditional probability?

Which machine learning model creates a model that predicts the value of a target variable by learning simple decision rules inferred from the data features?

Which machine learning algorithm creates different trees and every decision tree has a vote in the final decision?

In the context of machine learning, which of the following models assumes that the predictors have an equal effect on the outcome?

Which of the following machine learning algorithms makes decisions based on entropy and information gain?

Which machine learning algorithm, despite its simplicity, can outperform other complex algorithms in the case of text classification problems?

Which machine learning algorithm would be the most appropriate if prior probabilities are important for class prediction?

Which algorithm tends to overfit when dealing with noisy or complex data?

Which algorithm generates a multitude of decision trees at the time of training and outputs the class with the highest mode from these trees?