A new method has been created by a team of researchers to analyse big data that better predicts outcomes in health care, politics and other fields.
In an effort to reduce the error rate with methods like using a significance-based criterion for evaluating variables to find highly predictive variables, researchers at Princeton, Columbia and Harvard universities in the US proposed a new measure called the influence score, or I-score, to better measure a variable's ability to predict.
‘The I-score is effective in differentiating between noisy and predictive variables in big data and can significantly improve the prediction rate.’
In their study, to be published in journal Proceedings of the National Academy of Sciences, researchers found that the I-score is effective in differentiating between noisy and predictive variables in big data and can significantly improve the prediction rate.
"The practical implications are what drove the project, so they are quite broad," lead author Adeline Lo said.
"That the I-score fares especially well in high dimensional data and with many complex interactions between variables is an extra boon for the researcher or policy expert interested in predicting something with large dimensional data," Lo, who is a postdoctoral researcher in Princeton's Department of Politics, added.
The I-score improved the prediction rate in breast cancer data from 70 percent to 92 percent. The I-score can be applied in a variety of fields, including terrorism, civil war, elections and financial markets, the researchers said.