Data streams pose the problem that you cannot evaluate as you would do when working on a complete in-memory dataset. For a correct and optimal approach to feed your SGD out-of-core algorithm, you first have to survey the data (by taking a chuck of the initial instances of the file, for example) and find out the type of data you have at hand.
We distinguish among the following types of data:
Quantitative values
Categorical values encoded with integer numbers
Unstructured categorical values expressed in textual form
When data is quantitative, it could just be fed to the SGD learner but for the fact that the algorithm is quite sensitive to feature scaling; that is, you have to bring all the quantitative features into the same range of values or the learning process won't converge easily and correctly. Possible scaling strategies are converting all the values in the range [0,1], [-1,1] or standardizing the variable by centering its mean to zero and converting...
