Virtual Labs

Naïve Bayes Classification for Text and Categorical Data using Prior and Posterior Probabilities

Naive Bayes is classified as which type of machine learning algorithm?

a: Unsupervised learning algorithm Explanation

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b: Reinforcement learning algorithm Explanation

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c: Probabilistic learning algorithm Explanation

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d: Semi-supervised learning algorithm Explanation

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The fundamental principle behind the Naive Bayes classifier is based on:

a: Maximum margin optimization Explanation

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b: Bayes’ theorem Explanation

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c: Gradient descent Explanation

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d: Euclidean distance Explanation

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Why does Naive Bayes often perform well on high-dimensional text data despite its strong independence assumption?

a: Because text features are always independent Explanation

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b: Because relative probability comparisons matter more than exact probability values Explanation

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c: Because it automatically removes correlated features Explanation

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d: Because it internally uses deep learning layers Explanation

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What are the assumptions of Naïve Bayesian classifier?

a: It assumes that features of a data are completely dependent on each other Explanation

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b: It assumes that each input variable is dependent and the model is not generative Explanation

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c: It assumes that each input attributes are independent of each other and the model is generative Explanation

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d: It assumes that the data dimensions are dependent and the model is generative Explanation

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In Naive Bayes text classification, why is Laplace smoothing applied during probability estimation?

a: To reduce training time Explanation

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b: To handle zero probability for unseen words Explanation

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c: To normalize feature values Explanation

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d: To remove noisy features Explanation

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Which of the following statement is not true about Naïve Bayes classifier algorithm?

a: It cannot be used for Binary as well as multi-class classifications Explanation

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b: It is the most popular choice for text classification problems Explanation

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c: It performs well in Multi-class prediction as compared to other algorithms Explanation

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d: It is one of the fast and easy machine learning algorithms to predict a class of test datasets Explanation

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From a probabilistic modeling perspective, why is Naive Bayes considered a generative model?

a: It directly models the decision boundary between classes Explanation

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b: It generates new synthetic training samples Explanation

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c: It models the joint probability distribution of features and class labels Explanation

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d: It maximizes classification accuracy during training Explanation

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Which one of the following applications is not an example of Naïve Bayes algorithm?

a: Spam filtering Explanation

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b: Text classification Explanation

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c: Stock market forecasting Explanation

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d: Sentiment analysis Explanation

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Which situation most strongly violates the core assumption of the Naive Bayes classifier?

a: Features having different numeric scales Explanation

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b: Highly correlated features influencing the same class Explanation

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c: Presence of class imbalance in the dataset Explanation

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d: Large number of training samples Explanation

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During text classification, what does the word “feature” usually represent?

a: Sentence structure Explanation

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b: Word or term occurrence Explanation

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c: Grammar rules Explanation

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d: Punctuation marks Explanation

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