How can you tell if a model meets the requirements for proportionality and additivity?
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How can you tell if a model meets the requirements for proportionality and additivity?
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- Using a clear picture, describe the iterative process of calibrating a model.Does a higher value of k lead to a more complex model, or a less complex model?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) Group of answer choices True False
- If a model is prescriptive, what makes it so?Does having a high bias underfits data and produces a model that is too simple and general, while having a high variance overfits data and produces a model that is too complex and specific?With neat diagram, explain the iterative process of calibrating a model.
- Use a simple example to explain the iterative nature of the model calibration process.In 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?ER 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?