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verified
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Multiple Choice
A) association rule mining.
B) cluster analysis.
C) decision trees.
D) artificial neural networks.
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verified
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verified
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True/False
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verified
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Multiple Choice
A) its ability to predict the outcome of a previously unknown data set accurately
B) its speed of computation and computational costs in using the mode
C) its ability to construct a prediction model efficiently given a large amount of data
D) its ability to overcome noisy data to make somewhat accurate predictions
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verified
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verified
Multiple Choice
A) the ratio of correctly classified positives divided by the total positive count.
B) the ratio of correctly classified negatives divided by the total negative count.
C) the ratio of correctly classified positives divided by the sum of correctly classified positives and incorrectly classified positives.
D) the ratio of correctly classified positives divided by the sum of correctly classified positives and incorrectly classified negatives.
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verified
Multiple Choice
A) associations
B) visualization
C) classification
D) clustering
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Multiple Choice
A) because the hardware exists in most organizations and it is available to use
B) because the most of the algorithms used for data mining require it
C) because of the massive data amounts and search efforts involved
D) because any strategic application requires parallel processing
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verified
True/False
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Essay
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True/False
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Multiple Choice
A) understanding the business goal is critical.
B) understanding the data, e .g., the relevant variables, is critical to success.
C) building the model takes the most time and effort.
D) data is typically preprocessed and/or cleaned before use.
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Multiple Choice
A) SEMMA
B) proprietary organizational methodologies
C) KDD Process
D) CRISP-DM
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