When developing machine learning projects is expected that the investment creates value for an organization. How can we measure the ROI of an investment in a machine learning project? For example, when we measure the dropout, should we consider the total cost of dropout and estimate gains, reducing it, for example, 5% to justify the investment and measure somehow the ROI?
Is there any approach to consider to estimate the ROI? Research addressing this the investment in the context of machine learning and the expected value generation to an organization?
Hi.,
To determine the return as a hard number, determine the profit from the project and subtract the costs. The final figure is the money made from the project. To determine the percentage return, divide the hard number from the latter calculation by the original cost.
For more info:
https://www.mariner-usa.com/blog/roi-machine-learning/
As much as I know ... Companies define their metrics - for example metrics related to number of clicks they receive after changing or applying a model. And the ab testing based metric improvement + resources and time utilization can help you calculate ROI
Khushboo Thaker Jaydip Datta C K Gomathy Thanks for your comments. To develop projects and justify to managers there is a need to explore the financial outcomes of the project, e.g. when netflix created a competition with 1 million dollar prize for algorithm to increase the accuracy of the company's recommendation engine by 10 percent, they expect outcomes to develop this. Considering that a project should be developed to contribute to a business objective (at least is developed with that in mind), the machine learning ROI could be measured according to its contribution to a business objective, e.g. reduce dropout in 5% and this could be considered the effect to achieve the objective. Is there any paper addressing how to evaluate or estimate the ROI in machine learning projects and investments? Or the investments being made by the organizations in this area?
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