Photography is the activity of recording precious moments which are often difficult to make up afterwards. Therefore, taking the correct picture under proper guidance assistance is important. Although there are many factors that can determine a good photo, in general, photos that do not follow the composition rules usually look bad that make the viewer feel uncomfortable. As a solution, in this paper, we propose an intelligent photographing guidance system using machine learning. The guidance is based on interpretable models that can give reasons for decisions. There are two kinds of features for guidance, which are traditional image features and deep features. Traditional features includes saliency map, sharpness map, prominent lines, and each of them are in multi-scale Gaussian pyramid. Deep feature is extracted during the establishment of a CNN based image composition classifier. We use these two kinds of features as inputs for tree based interpretable machine learning model to establish a feasible photographing guidance system. The guidance system references our composition classifier with 94.8% of accuracy, which the tree based interpretable model is capable of guiding camera users to alter image contents for obtaining better aesthetical compositions to take photos of good quality.