Simon Chan
Simon is the CEO and co-founder of PredictionIO — an open source Machine Learning Server that empowers programmers and data engineers to build smarter applications. Simon founded three tech startups in the past 10 years, in the bay area, in Hong Kong and in Mainland China. He specializes in machine learning and recommendation technology, with a strong interest in social applications. Simon holds a B.S.E. in Computer Science from University of Michigan and is a PhD candidate in Machine Learning at University College London.
A good nudge trumps a good prediction
Identifying the right evaluation methods is essential to successful machine learning.
Editor’s note: this is part of our investigation into analytic models and best practices for their selection, deployment, and evaluation.
We all know that a working predictive model is a powerful business weapon. By translating data into insights and subsequent actions, businesses can offer better customer experience, retain more customers, and increase revenue. This is why companies are now allocating more resources to develop, or purchase, machine learning solutions.
While expectations on predictive analytics are sky high, the implementation of machine learning in businesses is not necessarily a smooth path. Interestingly, the problem often is not the quality of data or algorithms. I have worked with a number of companies that collected a lot of data; ensured the quality of the data; used research-proven algorithms implemented by well-educated data scientists; and yet, they failed to see beneficial outcomes. What went wrong? Doesn’t good data plus a good algorithm equal beneficial insights? Read more…