Towards Bridging the Gap between Machine Learning Researchers and Practitioners

As data keeps growing, Big Data starts to be everywhere, and there is almost an urgent need to make sense of this data. This is why Machine Learning has become crucial as it aids in improving business, decision making and it has the potential to provide solutions for a wide range of problems in computer science and other fields. Machine Learning (a.k.a. Data Mining or Predictive Analytics) algorithms can learn how to perform certain tasks by generalizing from the out of sample examples. This is a totally different paradigm than traditional programming language approaches based on writing programs that process data to produce an output. However, choosing a suitable machine learning algorithm for a particular application requires substantial amount of effort that is even hard to undertake even with text books. In order to reduce the effort, this paper introduces a recommender system that will aid machine learning researchers and practitioners to choose the optimum machine learning model to use. The system is based on an approach that is introduced in the paper called TCDC which stands for Train, Compare, Decide, and Change.