7 Machine Learning Mistakes To Avoid

However, mastery in the subject can only be achieved by adding profundity to one's knowledge. One such facet involves learning how to deal with the assumptions and drawbacks of the various algorithms being used. In a post for KDnuggets, Ex-Google engineer Cheng-Tao Chu goes into seven mistakes to avoid for the aspiring Machine Learning expert. Among his seven points, Chu talks about picking a suitable evaluation metric for your model that fits the domain in which it is being applied, being cognizant of and dealing with outliers carefully, and avoiding models which tend to overfit when dealing with data where the number of features outnumbers the number of data points.