Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
3rd Edition
ISBN: 9781118729274
Author: Galit Shmueli, Peter C. Bruce, Nitin R. Patel
Publisher: WILEY
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Chapter 2, Problem 6P
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Given: A specific company made the training data by using the internal data including the purchase and demographic information. The future data to be arranged will include the demographic data of the list of purchases from other sources. In the training data, the predictor, “refund issued�, was a useful predictor.
To find: The explanation on why the predictor named “refund issued� in the training data cannot be considered as an appropriate variable to be incorporated in the model...
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Chapter 2 Solutions
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
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Similar questions
- When building a data model, why is it sometimes preferable to leave out certain information?arrow_forwardIn what scenarios might a denormalized data model be preferred over a normalized one?arrow_forwardER modeling is one approach in creating a data model. There are several approaches in creating a data model (bottom up, top down, inside out, and mix). 1. explain each of these approaches!2. Then explain when we use top down, bottom up, inside out and mix? what is the function of each of the approaches?arrow_forward
- Study the details below and create ERD diagram for each question below. Drone / Video: a Drone may take many Videos; a Video is taken by one Drone. A Video instance is optional for a Drone; a Drone instance is required for a Video. A Facility may be recorded by many Videos; a Video records one Facility. A Video instance is optional for a Facility; a Facility instance is required for a Video. A Video may be the basis of many Analyses; an Analysis is based on one Video. An Analysis instance is optional for a Video; a Video instance is required for an Analysisarrow_forwardWhat is the difference between creating a data model from scratch and using one that has already been created?arrow_forwardThe analytical method relies heavily on the use of data modelling. In order to build a model, we'll need some data.arrow_forward
- How do you represent one-to-many and many-to-many relationships in a data model?arrow_forwardIn the event that you are operating a clinic that treats patients of various kinds, you should infer the best data structure to use. It is a decent approach to treat patients on a first come, first served basis, but is it the best strategy?arrow_forwardWhich model is overfitted on training data and what is the best metric to check the generalizability of the models? RMSE 2 0 1 model 4-Test RMSE model 4- CV RMSE model 5-Test RMSE model 5- CV RMSE 2 3 4 5 LO Training RMSE CV RMSE Test RMSEarrow_forward
- When building a data model, why is it sometimes preferable to not include a certain piece of information?arrow_forwardCreate an ER model to represent the data used by the library.The library provides books to borrowers. Each book is described by title, edition, and year of publication, and is uniquely identified using the ISBN. Each borrower is described by his or her name and address and is uniquely identified using a borrower number. The library provides one or more copies of each book and each copy is uniquely identified using a copy number, status indicating if the book is available for loan, and the allowable loan period for a given copy. A borrower may loan one or many books, and the date each book is loaned out and is returned is recorded. Loan number uniquely identifies each book loan.arrow_forwardModel evaluation Create a predictions variable using your fitted model and the test dataset; call it y_pred. Then get the accuracy score of your predictions and save it in a variable called accuracy. Finally get the confusion matrix for your predictions and save it in a variable called confusion_mat. Code: y_pred = Noneaccuracy = Noneconfusion_mat = Nonearrow_forward
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