That's true, but to have the best chance of designing a good method/analysis, I need to know what the variables in my analysis mean. Otherwise, it is tougher to make decisions about what variables it makes sense to include in a model, what sorts of transformations make sense, what sort of approaches might work best, etc.
I would echo this sentiment. Not only are the columns intentionally mis-labeled but they also appear to be computed, meaning some of the variance inherent to the original sample will have been lost.
Comments
Not necessarily. They might as well be named attribute_1, attribute_2....attribute_n. ML algorithms don't care about the meaning of the features.
That's true, but to have the best chance of designing a good method/analysis, I need to know what the variables in my analysis mean. Otherwise, it is tougher to make decisions about what variables it makes sense to include in a model, what sorts of transformations make sense, what sort of approaches might work best, etc.
I would echo this sentiment. Not only are the columns intentionally mis-labeled but they also appear to be computed, meaning some of the variance inherent to the original sample will have been lost.