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 10P
Two models are applied to a dataset that has been partitioned. Model A is considerably more accurate than model B on the training data, but slightly less accurate than model B on the validation data. Which model are you more likely to consider for final deployment?
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The hyper-parameters of a model must NOT be tuned on the test data ( i.e, the data used to evaluate the performance of the final model after selecting the hyper-parameters)
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Only by seeing how well a model performs on test data can its accuracy be determined. elaborate on; provide additional information about Explain?
Use a simple example to explain the iterative nature of the model calibration process.
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Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
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- A data transformation might be as simple as a new representation of the data, or it could include a complex integration approach involving many different data sources. Should we take this assertion seriously and treat it as true or false?arrow_forwardWhen evaluating the correctness of a model, the only thing that can be considered is how well the model performs on test data. explain in detail, expand on? Explain?arrow_forwardA model can only be evaluated based on its performance on test data. explain in detail, expand on? Explain?arrow_forward
- Using a clear picture, describe the iterative process of calibrating a model.arrow_forwardGive a concise description of model calibration as an iterative process and provide an example.arrow_forwardWhat is the difference between creating a data model from scratch and using one that has already been created?arrow_forward
- A model's correctness can only be evaluated by its performance on test data. describe in depth; provide more information? Explain?arrow_forwardIn what way does the process of analytical reasoning depend on data modeling? Is there a process that can be used to figure out the ideal number of observations that should be included into our model?arrow_forwardWhat is a training dataset? * a) A part of the dataset used to validate the test results b) A part of the dataset used to test how well the model can predict c) A dataset that we use to build the mode and have it self-learn d) A subset of the dataset that makes the dataset less skewedarrow_forward
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