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Explain the difference between a training set and a testing set. Why do we need to differentiate them? Can the same set be used for both purposes? Why or why not?

Basically, you have three data sets: training, validation, and testing.

You train the classifier using ‘training set’, tune the parameters using ‘validation set’ and then test the performance of your classifier on unseen ‘test set’. An important point to note is that during training the classifier only the training and/or validation set is available. The test set must not be used for training the classifier. The test set will only be available during testing the classifier.

 

There is no ‘one’ way of choosing the size of training/testing set and people to apply heuristics such as 10% testing and 90% training. However, doing so can bias the classification results and the results may not be generalizable. A well-accepted method is N-Fold cross-validation, in which you randomize the dataset and create N (almost) equal size partitions. Then choose Nth partition for testing and N-1 partitions for training the classifier. Within the training set, you can further employ another K-fold cross validation to create a validation set and find the best parameters. And repeat this process N times to get an average of the metric. Since we want to get rid of classifier ‘bias’ we repeat this above process M times (by randomizing data and splitting into N-fold) and take an average of the metric.  Cross-validation is almost unbiased, but it can also be misused if training and validation set comes from different populations and knowledge from the training set are used in the test set.

 

What is the relationship between environmental analysis and problem identification?

 

There is a direct relationship between environmental analysis and problem identification. A manager scans the environment to identify any problems that may exist and require addressing. In a way, the manager is “looking for trouble” (or opportunities!). Explain the differences between static and dynamic models. How can one evolve into the other?

 

A static model does not account for the element of time, while a dynamic model does. Static models typically use static aging techniques, changing certain variables on the original microdata file to produce a file with the demographic and economic characteristics expected in the future year. “Dynamic” models age each person in the microdata file from one year to the next by probabilistically deciding whether or not that person will get married, get divorced, have a child, drop out of school, get a job, change jobs, become unemployed, retire, or die.

 

Static and dynamic models each have their own strengths. Dynamic models feature more detailed and realistic population aging. Although dynamic models have been used to age microdata files 50 or more years into the future, the simpler aging procedures in static models are generally only applied to estimates for the near future. Dynamic models are often viewed as better able to produce realistic long-range estimates, which account for interim shifts in economic and demographic trends. The advantage of static models is their very detailed program simulations. The more simplified program simulations in dynamic models may not incorporate the detailed program rules that are often the basis of policy changes. Future models might include both detailed dynamic aging and detailed program simulations.

 

What is the difference between an optimistic approach and a pessimistic approach to decision making under assumed uncertainty?


Most of the rules for decision making under uncertainty express a different degree of decision maker´s optimism. Maximax criterion is an optimistic approach where the decision maker determines the maximum payoff for each act and then an act is selected which provides the highest returns.

On the other hand, Maximum is a pessimistic approach which maximizes the minimum payoff or consequence for every alternative.

Explain why solving problems under uncertainty sometimes involves assuming that the problem is to be solved under conditions of risk.


Decision making under uncertainty and risk entails the selection of a course of action when we do not know with certainty the results that each alternative action will lead. Furthermore, we assume that the outcome of whatever course of action we select is affected only by chance and not by opponent or competitor. Whenever possible, we attempt to determine (or estimate) the probabilities of states of nature. In this case, we are talking about decision making under risk.

 

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