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Stochastic frontier analysis for Machine Learning Program Evaluation
Stochastic frontier analysis (SFA) has been increasingly used to measure efficiency within data science. A method is build for measuring efficiency in an empirical before-and-after setting which is an obvious choice for companies because the rich advantages of company data.
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Stochastic frontier analysis (SFA) has been increasingly used to measure efficiency within data science. A method is build for measuring efficiency in an empirical before-and-after setting which is an obvious choice for companies because the rich advantages of company data.

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Go to the profile of Kristian Nørgaard Larsen