Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/3511
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DC Field | Value | Language |
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dc.contributor.author | Kira Radinsky | |
dc.contributor.author | Yoni Acriche | |
dc.date.accessioned | 2020-05-07T16:08:28Z | - |
dc.date.available | 2020-05-07T16:08:28Z | - |
dc.date.issued | 2016-01-01 | |
dc.identifier.citation | Unlimited Distribution | |
dc.identifier.uri | https://dair.nps.edu/handle/123456789/3511 | - |
dc.description | https://hbr.org/2016/12/how-to-make-better-predictions-when-you-dont-have-enough-data | |
dc.description.abstract | Predictive statisticians in the private sector face similar problems when trying to predict unexpected events, or when working from flawed or incomplete data. Simply turning the work over to machines won't help: most machine learning and statistical mining techniques also hold the assumption that historical data, which is used to train the machine-learning model, behaves similarly to the target data, to which the model is later applied. However, this assumption often does not hold as the data is obsolete, and it is often expensive or impractical to get the additional recent data that holds this assumption. | |
dc.language | English (United States) | |
dc.publisher | Harvard Business Review | |
dc.relation.ispartofseries | Innovation | |
dc.relation.ispartofseries | SEC809-MKT-16-0048 | |
dc.subject | Strategy | |
dc.subject | Business Process | |
dc.subject | Innovation | |
dc.subject | Agile | |
dc.subject | Dynamic Marketplace | |
dc.subject | Data | |
dc.subject | Transfer Learning | |
dc.subject | Algorithm | |
dc.subject | Artificial Intelligence | |
dc.title | How to Make Better Predictions When You Do not Have Enough Data | |
dc.type | Article | |
Appears in Collections: | Section 809 Panel: Reports, Recommendations & Resource Library |
Files in This Item:
File | Size | Format | |
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SEC809-MKT-16-0048.pdf | 179.69 kB | Adobe PDF | View/Open |
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