Abstract
In the data understanding phase we have explored all available data and carefully checked if they satisfy our assumptions and correspond to our expectations. We intend to apply various modeling techniques to extract models from the data. Although we have not yet discussed any modeling technique in greater detail (see Chaps. 7ff), we have already glimpsed at some fundamental techniques and potential pitfalls in the previous chapter. Before we start modeling, we have to prepare our data set appropriately, that is, we are going to modify our dataset so that the modeling techniques are best supported but least biased.
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Notes
- 1.
The reduction of inflected or derived words to their root (or stem) is called stemming. So-called stemmers are computer programs that try to automate this step.
- 2.
Bayesian classifiers can handle numerical data directly by imposing some assumptions on their distribution. If such assumptions cannot be justified, discretization may be a better alternative.
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Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. (2010). Data Preparation. In: Guide to Intelligent Data Analysis. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-84882-260-3_6
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