Suppose you have implemented regularized logistic regression to classify what object is in an image. However, when you test your hypothesis on a new set of images, you find that it makes unacceptably large errors with its prediction on the new images. You find out that at the same time, your hypothesis performs well with low error on the training set. Discuss what possibly the problem that you are facing and what are the solution that you may take to solve this issue