I’ve always used highly flexible machine learning algorithms like boosted trees, support vector machines, and random forests that supposedly excel at identifying non-linear and irregular patterns and variable interactions. I’ve been building predictive algorithms for years with reasonable success, but I’ve never really invested much time in visualizing my data -- no scatter plots, charts, or bar graphs.
When working on a new problem, I try to understand my data by reading about the topic for many hours and absorbing other people’s subject matter expertise. People have analyzed my data for decades, so there is no shortage of expert opinions. When coming up with new ideas for predictors, am I better off reading more expert opinions because machine learning algorithms will generally "outperform my eyes"?
If I'm going to test the inclusion of each candidate variable based on the performance of out-of-sample data anyway, do visualization techniques like box plots become redundant? If a box plot says that a certain variable is important, wouldn't a boosted trees model or support vector machine reach the same conclusion?
My models are generally boosted trees. Are there certain visualization techniques that would help me see things that boosted trees could not?