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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Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
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- Use a simple example to explain the iterative nature of the model calibration process.arrow_forwardThe 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) Group of answer choices True Falsearrow_forwardGive a concise description of model calibration as an iterative process and provide an example.arrow_forward
- When 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_forwardWhat are some creative ways to use binary variables in model formulation?arrow_forward10-fold cross-validation is applied to a dataset containing 150 examples. State (i) the number of models that are trained and evaluated, (ii) the number of examples for training a model, and (iii) the number of examples for testing a modelarrow_forward
- Model 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_forwardHow do you choose the best Linear Regression training strategy to utilise when you have a large training set with millions of features?arrow_forwardWhen it comes to doing static analysis in-house, what are the benefits and drawbacks?arrow_forward
- In the first place, why should the data be split into training and validation sets? When it comes down to it, what will the training set be used for? The validation set's purpose is yet unknown.arrow_forwardIn classification and regression trees (CART), it is done by the model itself, based on how dirty it is. Features that are used in CART are thought to be the most important parts of the tool. Some experts said that people should not have to choose features before they build CART. However, some other analysts disagreed and said that, as long as we need to run models, feature selection is still an important step before building a model. Before running CART models, do you think it is important for users to pick out the features they want to use?arrow_forwardWhat is the difference between creating a data model from scratch and using one that has already been created?arrow_forward
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