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Presenting dimensionality reduction as the starting point in machine learning is not usual, but it does make sense, technically. A classification model can be s
by rsrsrs86 5y ago
Presenting dimensionality reduction as the starting point in machine learning is not usual, but it does make sense, technically. A classification model can be seen as mapping inputs from an N dimensional space to set of labels. Regression maps N dimensional spaces to the real line and so on. Even a sample mean reduces a set with dimensionality equal to the sample size to a single real number.
- jrumbut 5y agoYou're right but really the sheet is just a mess. Naive Bayes is on both sides of "Data Too Large" and the wrong side of "Explainable." It's almost like they put some algorithm names and machine learning concepts in a hat and drew them at random.