Let's say that I have training data and test data. I trained the model with the training data and then constructed a confusion matrix of the predictions of both. I am getting that the prediction of training data is lower than the test data.
This my training data prediction:
This is the test prediction confusion matrix (bottom screen). What does it mean? Is my model bad because my training accuracy is lower than my test? What about my 95% CI which is lower in the test data than in the train data prediction? I do not really understand everything even after searching on Google. I am using linear SVM method.
If anyone knows something I would be happy to listen to it.