Logistic regression differs from the Regression techniques in the use of the statistical measures. Regression is very useful to calculate a linear relationship between a dependent and independent variable, and then use that relationship for prediction. IT service requests demonstrate elongated scatter plots in specific categories. Even when the service requests come demanding different resolutions in the same category, the relief times are bounded and can be plotted along the timeline. One of the best advantages of linear regression is the prediction about time as an independent variable. When the data point has many factors contributing to their occurrence, a linear regression gives an immediate ability to predict where the next occurrence may happen. This is far easier to do than come with up a model that behaves like a good fit for all the data points.
Another use of statistical regression technique in the data mining of IT service tickets is the case when the factors are beyond the control of the IT department such as holidays, human resources, response to high severity incidents and outages, critical vulnerability response and other parameters that are not part of the routine. It is easier to parameterize these for their probabilities and compare them with a model that otherwise considers only the routine response times.
Thus, we see that the choice of a data mining algorithm is strictly based on the articulation of its associated use case.
Comparisions between other high-level algorithms are described here: https://1drv.ms/w/s!Ashlm-Nw-wnWxBFlhCtfFkoVDRDa?e=aVT37e
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