Cross validation error meaning in decision tree program

I need help deciphering what a cross valiadation error (kfoldloss) of 536 means. I've developed a program that creates a decision tree. The program runs with 2,286 data points for several different variables. Does this mean that the model gets 536 predictions incorrect?

Attached is the code. Thanks for your responses and help.

Answer 1

[Alain Kuchta]

You may find the explanation of Classification Loss from the documentation helpful in understanding this measure:

http://www.mathworks.com/help/stats/classificationpartitionedmodel.kfoldloss.html#bswic2v-2