What do this Classification evaluation results mean to you? Do they are suspicious or not? I have collected dataset with two class labels and used the SVM Method to classify the dataset, and this is the results. Does this appear suspicious or not?

scikit-learn classifiers with SVM SVC train on 114859 instances, test on 49227 instances
Excution (Training) Time:  9.82799983025
Excution (Testing) Time:  3.75
accuracy: 0.999837487558
Precision-Recall AUC: 1.00
ROC AUC: 1.00
pos precision: 0.999822253822
pos recall: 1.0
pos F-measure: 0.999911119012
neg precision: 1.0
neg recall: 0.998107404779
neg F-measure: 0.999052806062
The statistics here are obviously very good, in fact too good for any practical data set. Your model is almost perfect... Unfortunately, it's practically useless and I'll explain.

In machine learning, if you see something like this you know you are in trouble. That can happen if there are problems with your data workflow. For example, you might have removed all outliers that you shouldn't, or you actually used a subset of your training data for the test set.

It's fine if you're just toying SVM, but you'll never encounter something like this in real life.