Hands-On Machine Learning with C++

Data scaling and standardization

All users evaluate (rate) items differently. If someone puts 5s in a row, instead of waiting for 4s from someone else, it's better to normalize the data before calculating it—that is, convert the data to a single scale, so that the algorithm can correctly compare the results with each other. Naturally, the predicted estimate then needs to be converted to the original scale by inverse transformation (and, if necessary, rounded to the nearest whole number).

There are several ways to normalize data, detailed as follows:

• Centering (mean-centering): From the user's ratings, subtract their average rating. This type of normalization is only relevant for non-binary matrices.
• Standardization (z-score): In addition ...