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State the answer whether the statement is (T) for True of (F) for False in the blank. 1. Data mining can be said to be a process design to detect patterns in dataset. In SEMMA tools of data mining process the M are refers to Modify where data is modified by creating and transforming the variables to serve for model selection. 2. 3. Supervised data mining refers to the problem of finding abstracted patterns (or structures) in the unlabeled data. 4. Education level is an example of ordinal type of data.

State the answer whether the statement is (T) for True of (F) for False in the blank. 1. Data mining can be said to be a process design to detect patterns in dataset. In SEMMA tools of data mining process the M are refers to Modify where data is modified by creating and transforming the variables to serve for model selection. 2. 3. Supervised data mining refers to the problem of finding abstracted patterns (or structures) in the unlabeled data. 4. Education level is an example of ordinal type of data.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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State the answer whether the statement is (T) for True of (F) for False in the blank. 1. Data mining can be said to be a process design to detect patterns in dataset. In SEMMA tools of data mining process the M are refers to Modify where data is modified by creating and transforming the variables to serve for model selection. 2. 3. Supervised data mining refers to the problem of finding abstracted patterns (or structures) in the unlabeled data. 4. Education level is an example of ordinal type of data. 5. Training dataset is used to build initial predictive model and comprised bigger portion of data set. 6. Validation data set is used to measure/calculate the effectiveness of the model and comprised bigger portion of the data. 7. Example of data mining goal is to predict churn customer. 8. The area under the curve (AUC) will indicate the accuracy of the model The default value in SAS EMiner for interval variable imputation is count, while for class variable is mean. 10. Overfitting refers to a model that fits your training data too well.
Transcribed Image Text:State the answer whether the statement is (T) for True of (F) for False in the blank. 1. Data mining can be said to be a process design to detect patterns in dataset. In SEMMA tools of data mining process the M are refers to Modify where data is modified by creating and transforming the variables to serve for model selection. 2. 3. Supervised data mining refers to the problem of finding abstracted patterns (or structures) in the unlabeled data. 4. Education level is an example of ordinal type of data. 5. Training dataset is used to build initial predictive model and comprised bigger portion of data set. 6. Validation data set is used to measure/calculate the effectiveness of the model and comprised bigger portion of the data. 7. Example of data mining goal is to predict churn customer. 8. The area under the curve (AUC) will indicate the accuracy of the model The default value in SAS EMiner for interval variable imputation is count, while for class variable is mean. 10. Overfitting refers to a model that fits your training data too well.
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