I have 3 cases:
- I have a classification model that will be used to classify cats and dogs. On my train data dog pictures has a watermark on them, but cat pictures don't. The problem is: Whenever I have a watermark on a cat picture, the model will predict the cat picture as a dog picture
- I have another classification model that classifies questions and normal sentences. But whenever I have the "how" word in my normal sentence, the model will classify it as "question"
- I have a prediction model. I have 5 columns but column number 3 is very important. I mean the importance of that column is very high. But my model cannot understand it.
All of those problems have 1 common problem. The importance of "something" or "feature" is being misunderstood by models. How these kinds of problems can be solved?