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Discussion Question 1 & 2

Discussion Question 1

A banking company wants to build a neural network to predict who will default on 30-year fixed-rate home mortgage loans. Historically, approximately 2.5% of individuals default.

Given the small percentage of defaulters, what are some of the problems that may be encountered when fitting a neural network model? Is this a problem specific to neural networks, or is this a problem other modeling techniques have as well? What are some of the solutions that can be implemented to overcome the insufficient minority class problem? Provide two or three examples.

Discussion Question 2

You previously built a model that was designed to predict when customers would default. It included macro-economic variables such as unemployment rate, GDP, and other market indicators. When the model was initially built, it proved to be quite accurate.

One year later, the model’s accuracy has decreased significantly. What could have occurred during that time to reduce the efficacy of the model? When building models, should one expect them to work indefinitely? Why? What could be done to keep the model current? Provide two or three examples.

Last Updated on February 11, 2019

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