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    Question

    Which machine learning model is most appropriate for

    detecting spam emails, considering its ability to handle high-dimensional data and probabilistic predictions?
    A K-Nearest Neighbors (KNN) Correct Answer Incorrect Answer
    B Decision Trees Correct Answer Incorrect Answer
    C Naive Bayes Correct Answer Incorrect Answer
    D Support Vector Machines (SVM) Correct Answer Incorrect Answer
    E Linear Regression Correct Answer Incorrect Answer

    Solution

    Naive Bayes is ideal for spam email detection due to its simplicity and efficiency in handling high-dimensional data. 1. Probabilistic Modeling: Based on Bayes’ theorem, it calculates the probability of an email being spam given certain features like word frequency. 2. High-Dimensional Data: Naive Bayes performs well with sparse data, such as word occurrences in text. 3. Scalability: It is computationally efficient and scales well for large datasets. 4. Robustness: Despite its "naive" assumption of feature independence, it achieves high accuracy in text classification tasks. Why Other Options Are Incorrect: • A) KNN: Inefficient for large datasets and high-dimensional spaces like text. • B) Decision Trees: Prone to overfitting and less effective with sparse data. • D) SVM: Effective but computationally expensive for large datasets. • E) Linear Regression: Unsuitable for classification tasks like spam detection.

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