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    Question

    Which type of machine learning technique is best suited

    for supervised learning tasks? 
    A K-means Clustering Correct Answer Incorrect Answer
    B Decision Trees Correct Answer Incorrect Answer
    C Principal Component Analysis (PCA) Correct Answer Incorrect Answer
    D k-Nearest Neighbors (k-NN) Correct Answer Incorrect Answer
    E Support Vector Machines (SVM) Correct Answer Incorrect Answer

    Solution

    Decision trees are well-suited for supervised learning tasks, where the goal is to predict an output variable based on input features. The tree structure allows for easy interpretation of how the input variables are related to the output, making it ideal for classification and regression tasks. K-means Clustering: A clustering technique used in unsupervised learning. PCA: A dimensionality reduction technique, not a supervised learning algorithm. k-Nearest Neighbors (k-NN): While k-NN can be used for supervised learning, decision trees are generally more interpretable. Support Vector Machines (SVM): While SVMs are also used in supervised learning, decision trees offer more visual interpretability.

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