representing data and engineering features so farwe've assumed that our data comes in as two-dimensional array of floatingpoint numberswhere each column is continuous feature that describes the data points for many applicationsthis is not how the data is collected particularly common type of feature is the categorical features also known as discrete featuresthese are usually not numeric the distinction between categorical features and continuous features is analogous to the distinction between classification and regressiononly on the input side rather than the output side examples of continuous features that we have seen are pixel brightnesses and size measurements of plant flowers examples of categorical features are the brand of productthe color of productor the department (booksclothinghardwareit is sold in these are all properties that can describe productbut they don' vary in continuous way product belongs either in the clothing department or in the books department there is no middle ground between books and clothingand no natural order for the different categories (books is not greater or less than clothinghardware is not between books and clothingetc regardless of the types of features your data consists ofhow you represent them can have an enormous effect on the performance of machine learning models we saw in and that scaling of the data is important in other wordsif you don' rescale your data (sayto unit variance)then it makes difference whether you represent measurement in centimeters or inches we also saw in that it can be helpful to augment your data with additional featureslike adding interactions (productsof features or more general polynomials the question of how to represent your data best for particular application is known as feature engineeringand it is one of the main tasks of data scientists and machine