I am trying to use keras for multi label news classification. I am a beginner in machine learning so please bear with me.
Here is my training loss vs validation loss diagram:
I understand that my model is overfitting. On the test set, I have precision of 0.89 , recall of 0.82, f1score 0.83. This according to me is high precision and high recall. So this means a good classification right ?
Further, this is the parameter settings:
My question is why in the test set there is high precision and high recall but the model is overfitting in validation set. Isn't it odd? or am i missing something?
I would have uploaded my dataset. But the dataset excel file is 800 MB.
Note that I don't have a seperate training set, validation set and test set. I have a single file. On the first column are the articles and in the second columns are the labels.
I used sklearn.train_test_split twice so that the training set is 60% test set is 20% and validation set is 20%.