# Is it OK to mix categorical and continuous data for SVM (Support Vector Machines)?

I have a dataset like

+--------+------+-------------------+
| income | year |        use        |
+--------+------+-------------------+
|  46328 | 1989 | COMMERCIAL EXEMPT |
|  75469 | 1998 | CONDOMINIUM       |
|  49250 | 1950 | SINGLE FAMILY     |
|  82354 | 2001 | SINGLE FAMILY     |
|  88281 | 1985 | SHOP & HOUSE      |
+--------+------+-------------------+


I embed it into a LIBSVM format vector space

+1 1:46328 2:1989 3:1
-1 1:75469 2:1998 4:1
+1 1:49250 2:1950 5:1
-1 1:82354 2:2001 5:1
+1 1:88281 2:1985 6:1


Feature indices:

• 1 is "income"
• 2 is "year"
• 3 is "use/COMMERCIAL EXEMPT"
• 4 is "use/CONDOMINIUM"
• 5 is "use/SINGLE FAMILY"
• 6 is "use/SHOP & HOUSE"

Is it OK to train a support vector machine (SVM) with a mix of continuous (year, income) and categorical (use) data like this?

• You should spell out "SVM", at least once. – Peter Flom Feb 21 '13 at 1:28
• Make sure you scale that data! – Patrick Caldon Feb 21 '13 at 2:16