Researchers at Princeton, Columbia, and Harvard universities have developed a new method to analyze big data that better predicts outcomes in healthcare, politics, and other fields. In an effort to reduce the error rate in traditional methods, the researchers proposed a new measure called the influence score (I-score) to better measure a variable's ability to predict.
The researchers found the I-score is effective in differentiating between noisy and predictive variables in big data and can significantly improve the prediction rate. The I-score can be applied to a variety of fields, including terrorism, civil war, elections, and financial markets.
"Essentially, anytime you might be interested in predicting and identifying highly predictive variables, you might have something to gain by conducting variable selection through a statistic like the I-score, which is related to variable predictivity," says Princeton postdoctoral researcher Adeline Lo. "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."
From Princeton University
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Abstracts Copyright © 2016 Information Inc., Bethesda, Maryland, USA
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