Sometimes (well most of the time) using your favorite data mining methods and the more obvious attributes are not good enough. What to do then? An usual idea is to use every other models your software provides and/or add every attributes you could think of whatever their relation to your problem. In this post, I will try to elaborate a kind of “how to” for this case.
Step 1 : What is my model?
If your model is a neural network, it’s quite hard to get any insight of how it works by looking at the weights or neural functions. How could you improve something you don’t understand?
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When it comes to data mining the tool you use is very important. It seems that peoples use many software (see How many software packages is too much?). I’m currently using three tools : Weka, R and Microsoft Excel. When I have to, I’m also programming my own tools. Here is why I need all of them.
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It’s a damn good question which should be tightly correlated to your salary in an utopic world. In other words, how do you justify your existence?
Supposed you are doing a new product version (a software, a website, a car, whatever). It can be used internally to your enterprise or sell to client. In the later case, you guess you can look at the sales to see if your work was valuable. You can think the difference in benefits (positive at minimum) is a direct consequence to your work. Considering your salary and other costs, you can compute your ROI. If you don’t sale it, people which will use your new products could be instrumented, i.e. you can see if they improved theirs sales or benefits. Not so easy.
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