Q. What distinguishes unsupervised learning from supervised learning?

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Q. What is a common goal of unsupervised learning?

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Q. Why is machine learning well-suited for handling Big Data?

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Q. What types of tasks are strengths of machine learning algorithms in the context of Big Data?

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Q. What is the primary purpose of decision tree learning?

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Q. How are decision tree algorithms like ID3 and C4.5 used in the learning process?

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Q. What measures are used to determine the selection of attributes for splitting in decision trees?

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Q. What happens when an instance is incorrectly classified in the decision tree learning process?

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Q. What is one of the improved versions of the ID3 decision tree algorithm?

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Q. What does the "rxDTree" function in Revolution R* Enterprise 6.1 software provide for decision tree learning?

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Q. How does data parallelism work in the context of Big Data decision trees?

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Q. What is the potential drawback of using the "rxDTree" function for decision tree construction?

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Q. What are decision trees converted into to improve readability?

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Q. What is one way to obtain a more readable representation of a decision tree?

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Q. What is the covering approach in rule induction?

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Q. What is the main difference between clustering and classification?

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Q. How are clusters defined in clustering?

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Q. Which metric is commonly used to calculate distances in the k-means clustering algorithm?

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Q. What is the challenge associated with the k-means algorithm?

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Q. How can the challenge associated with the k-means algorithm be mitigated?

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