b. In preparation for regression, is any missing values imputation needed? If yes, should you do this imputation before generating the decision tree models? Why or why not? c. Add an Impute node to the diagram and connect it to the Data Partition node. Set the node to impute "U" for unknown class variable values, the overall mean for unknown interval variable values, and create imputation indicators for all imputed inputs. (1. Add a Regression node to the diagram and connect it to the Impute node. e. Choose the stepwise selection and validation error as the selection criterion. f. Run the Regression node and view the results. Which variables are included in the final model? Which variables are important in this model? What is the validation ASE? g. In preparation for regression, are any transformations of the data warranted? Why or why not? h. Disconnect the Impute node from the Data Partition node. Add a Transform Variables node to the diagram and connect it to the Data Partition node. Connect the Transform Variables node to the Impute node. i. Apply a log transformation to the DemAf, PromSpend, and PromTime inputs. j. Run the Transform Variables node. Explore the exported training data. Did the transformations result in less skewed distributions? k. Rerun the Regression node. Do the selected variables change? How about the validation ASE? 1. Create a full second-degree polynomial model. How does the validation average squared error for the polynomial model compare to the original model? 3. Predictive Modeling Using Neural Networks a. In preparation for a neural network model, is imputation of missing values needed? Why or why not? b. In preparation for a neural network model, is data transformation generally needed? Why or why not? c. Add a Neural Network tool to the Organics diagram. Connect the Impute node to the Neural Network node. (1. Set the model selection criterion to average squared error. e. Run the Neural Network node and examine the validation average squared error. How does it compare to other models? 4. Scoring Organics Data a. Create a Score data source for the ScoreOrganics data. b. Score the ScoreOrganics data using the model selected with the Model Comparison node. F. Rpttind Iln u: Initial Drninrt niadram I .ihrarv anrl natn inn'rn