Machine Learning Does Not Have All the Answers
November 25, 2016
Despite our broader knowledge, we still believe that if we press a few buttons and press enter computers can do all work for us. The advent of machine learning and artificial intelligence does not repress this belief, but instead big data vendors rely on this image to sell their wares. Big data, though, has its weaknesses and before you deploy a solution you should read Network World’s, “6 Machine Learning Misunderstandings.”
Pulling from Juniper Networks’s security intelligence software engineer Roman Sinayev explains some of the pitfalls to avoid before implementing big data technology. It is important not to take into consideration all the variables and unexpected variables, otherwise that one forgotten factor could wreck havoc on your system. Also, do not forget to actually understand the data you are analyzing and its origin. Pushing forward on a project without understanding the data background is a guaranteed fail.
Other practical advice, is to build a test model, add more data when the model does not deliver, but some advice that is new even to us is:
One type of algorithm that has recently been successful in practical applications is ensemble learning – a process by which multiple models combine to solve a computational intelligence problem. One example of ensemble learning is stacking simple classifiers like logistic regressions. These ensemble learning methods can improve predictive performance more than any of these classifiers individually.
Employing more than one algorithm? It makes sense and is practical advice why did that not cross our minds? The rest of the advice offered is general stuff that can be applied to any project in any field, just change the lingo and expert providing it.