Abstract
Machine learning, driven by recent advances in neural net technology, holds much promise, but how to validate any particular model? This note looks at why validation is necessary, and describes some practical techniques for doing so. The example scenario (from a Health Insurance survey, N = 29,145) is a sentiment analysis of verbatims arising from the Net Promoter Score expatiate ‘Why that rating?’ We show that respondents’ ratings can be predicted to a useful level of accuracy, and that the model can be validated by reference to external benchmarks.
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