There is a crucial distinction between the multitude of things we utilize in our daily lives and machine learning/high-dimensional data analysis: we aren't equipped to intuit the workings of high-dimensional advanced-math statistical inference in the same way that we can intuit the workings of say, a water pump, or simple arithmetic on Excel, or simple database systems, unless we are appropriately trained in the relevant math and science.
Some examples: blackbox application of classifiers (e.g. WEKA gui as used by some for data exploration) can ignore parameter optimization, unbalanced sets, parsimony in features, dimensionality reduction, etc. etc.
Some examples: blackbox application of classifiers (e.g. WEKA gui as used by some for data exploration) can ignore parameter optimization, unbalanced sets, parsimony in features, dimensionality reduction, etc. etc.