lesson 2 quiz

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Computer Science

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May 6, 2024

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1. The Data Exploration node in Model Studio enables you to do which of the following? a. Impute variables based on summary statistics. b. View the most important inputs or suspicious variables. c. See variables with a high percentage of nonmissing values. 2. To define variable metadata and assign rules to modify variables (for example, assigning a type of transformation), you can use either the Data tab or the Manage Variables node. a. True b. False 3. Which of the following statements is true about the Text Mining node? a. It processes audio and video data. b. It transforms a term-by-document frequency matrix using singular value decomposition (SVD) to create binary coefficients. c. It creates topics based on groups of terms that occur together in several documents. Each term-document pair is assigned a score for every topic. d. It does not allow terms and documents to belong to multiple topics. 4. After a pipeline is run, which of the following can you do using the Manage Variables node? a. Specify a different target variable. b. Modify the target variable attributes. c. Set up imputation and transformation rules. d. Perform imputation and transformations. 5. How do the transformations available in the Transformations node minimize bias in model predictions? a. by reducing the effect of extreme or unusual input values b. by replacing missing values and avoiding complete case analysis c. by converting unstructured data to structured data d. by reducing the total number of variables to reduce dimensionality

6. The Variable Selection node uses only supervised methods to select inputs. a. True b. False 7. Which of the following transformations creates bins for a numeric variable? a. inverse b. exponential c. standardize d. quantile 8. Which of the following statements is true about the validation data that the Variable Selection node creates from the training data? a. The Variable Selection node always creates these validation data. b. These validation data are used for variable selection during data preparation. c. These validation data are used for model assessment during the modeling process, instead of the original validation partition. 9. inputs during data preparation? a. A model that is based on a large number of inputs is very likely to be underfit to the training data. b. The more inputs you use to build the model, the more cases are required to discover the relationship between the inputs and the target. c. Modeling algorithms do not reduce the number of inputs. 10.Which of the following is a best practice for handling high-cardinality input variables? a. binning b. Winsorizing c. standardization d. text mining

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