I am watching the Data Mining with Weka videos and Prof. Ian Witten observed that Weka makes machine learning easy but:
The problem is understanding what it is that you have done.
That’s really the rub isn’t it? You loaded data, the program ran without crashing, some output was displayed.
All well and good but does it mean anything?
Or does your boss tell you what a data set will show after you complete machine learning on it?
Not to single out machine learning because there any number of ways to “cook” data long before it gets to the machine learning processor.
Take survey data for example. Where you ask some group of people for their responses.
A quick scan of survey methodology at Wikipedia and you will realize that services like Survey Monkey are for:
I’ve heard the arguments of no money to do a survey correctly so mid-management makes up questions that leads to the correct result. Business decisions are justified on that type of survey data.
Collecting data and running machine learning algorithms are vital day to day activities in data science.
Even if you plan to fool others, do be fooled yourself. Develop a critical outlook and questions that should be asked of data sets, depending upon their point of origin.
PS: Do you know of any courses on “data skepticism?” That would make a great course title. 😉