I am attempting to train an SVM from a set of features which are both numeric and categorical, for example:
- Distance X (Numeric)
- Distance Y (Numeric)
- Font Size Difference (Numeric)
- Word 1 Bold (Boolean)
- Word 2 Bold (Boolean)
- Word 1 Font Size (Numeric)
For mapping the features to a feature array I am treating true as 1 and false as 0 and then normalizing the entire feature array using Z-Scores.
Should I instead encode false as -1 so it has a numerical impact on the generation of the support vectors, with a value of 0 it does not modify the chosen vector (I assume)?
Should boolean features be normalized in the same way as numerics or should they be left with their encoded values?