Overfitting is a modeling error which occurs when a function is too closely fit to a limited set
of data points. Overfitting the model generally takes the form of making an overly complex model to
explain idiosyncrasies in the data under study.
Technically I think Overfitting in modeling is a problem, because the criterion used for selecting the
model is not the same as the criterion used to judge the suitability of a model. However, practically I
believe an “overfitting” model might produce its maximum performance.
For example, if the number of parameters is the same as or greater than the number of observations, then
a model can perfectly predict the training data simply by memorizing the data in its entirety. Such a
model, though, will typically fail severely when making predictions.
I would recommend to not only be aware of the size of data and number of parameters, but also the
conformability of the model structure with the data shape.
Essay 6. Discuss how you might use business analytics in your personal life, such as managing your grocery
purchasing, automobile maintenance, budgeting, sports, and so on. Be creative in identifying opportunities!
Essay 7. A supermarket has been experiencing long lines during peak periods of the day. The problem is
noticeably worse on certain days of the week, and the peak periods sometimes differ according to the day of
the week. There are usually enough workers on the job to open all cash registers. The problem the
supermarket faces is knowing when to call some of the workers who are stocking shelves up to the front to
work the checkout counters. How might business analytics help the supermarket? What data would be
needed to facilitate good decisions?