Naive-Bayes-Classifier | 朴素贝叶斯文本分类器 | Machine Learning library

I wanted to know if really AODE may be better than Naive Bayes in its way, as the description says:

https://cran.r-project.org/web/packages/AnDE/AnDE.pdf

--> "AODE achieves highly accurate classification by averaging over all of a small space."

https://www.quora.com/What-is-the-difference-between-a-Naive-Bayes-classifier-and-AODE

--> "AODE is a weird way of relaxing naive bayes' independence assumptions. It is no longer a generative model, but it relaxes the independence assumptions in a slightly different (and less principled) way than logistic regression does. It replaces the convex optimization problem used in training a logistic regression classifier by a quadratic (on the number of features) dependency on both training and test times."

But when I experiment it, I found that the predict results seems off, I implemented it with these codes:

I have applied svm on my dataset. my dataset is multi-label means each observation has more than one label.

while KFold cross-validation it raises an error not in index.

It shows the index from 601 to 6007 not in index (I have 1...6008 data samples).

This is my code: