A major hurdle for students and professional software developers who want to enter the world of machine learning (ML), is mastering not just the scientific background but also the available ML APIs. Therefore, we address the challenge of creating APIs that are easy to learn and use, especially by novices. However, it is not clear how this can be achieved without compromising expressiveness. We investigate this problem for scikit-learn, a widely used ML API. In this paper, we analyze its use by the Kaggle community, identifying unused and apparently useless parts of the API that can be eliminated without effecting client programs. In addition, we discuss usability issues in the remaining parts, propose related design improvements and show how they can be implemented by semi-automated wrapping of the existing third-party API.
NIKITA MEHROTRA Indraprastha Institute of Information Technology, NAVDHA AGARWAL Indraprastha Institute of Information Technology, Delhi, PIYUSH GUPTA Indraprastha Institute of Information Technology, Delhi, SAKET ANAND Indraprastha Institute of Information Technology, Delhi, David Lo Singapore Management University, Rahul Purandare IIIT-Delhi
Zhensu Sun The Hong Kong Polytechnic University, Li Li Monash University, Yan Liu Tongji University, Xiaoning Du Monash University, Australia, Li Li Monash University
NIKITA MEHROTRA Indraprastha Institute of Information Technology, NAVDHA AGARWAL Indraprastha Institute of Information Technology, Delhi, PIYUSH GUPTA Indraprastha Institute of Information Technology, Delhi, SAKET ANAND Indraprastha Institute of Information Technology, Delhi, David Lo Singapore Management University, Rahul Purandare IIIT-Delhi
