An algorithm can predict human behavior better than humans

You might presume, or at least hope, that humans are better at understanding fellow humans than machines are. But a new MIT study suggests an algorithm can predict someone’s behavior faster and more reliably than humans can.

Max Kanter, a master’s student in computer science at MIT, and his advisor, Kalyan Veeramachaneni, a research scientist at MIT’s computer science and artificial intelligence laboratory, created the Data Science Machine to search for patterns and choose which variables are the most relevant. Their paper on the project results (pdf) will be presented at the IEEE Data Science and Advanced Analytics conference in Paris this week.

It’s fairly common for machines to analyze data, but humans are typically required to choose which data points are relevant for analysis. In three competitions with human teams, a machine made more accurate predictions than 615 of 906 human teams. And while humans worked on their predictive algorithms for months, the machine took two to 12 hours to produce each of its competition entries.

For example, when one competition asked teams to predict whether a student would drop out during the next ten days, based on student interactions with resources on an online course, there were many possible factors to consider. Teams might have looked at how late students turned in their problem sets, or whether they spent any time looking at lecture notes. But instead, MIT News reports, the two most important indicators turned out to be how far ahead of a deadline the student began working on their problem set, and how much time the student spent on the course website. These statistics weren’t directly collected by MIT’s online learning platform, but they could be inferred from data available.

The Data Science Machine performed well in this competition. It was also successful in two other competitions, one in which participants had to predict whether a crowd-funded project would be considered “exciting” and another if a customer would become a repeat buyer.

Kanter told MIT News that there are many possible uses for his Data Science Machine. “There’s so much data out there to be analyzed,” he said. “And right now it’s just sitting there not doing anything.”

Article Disclaimer: This article was published by QUARTZ based on a research published by MIT and was retrieved on 10/19/2015 and posted here at INDESEEM for educational and information purposes only. The views, opinions, thoughts, and research findings are those of the authors on this article.

The Power and Flexibility of the Response Screening Platform in SAS JMP Pro

Response Screening in JMP Pro 12

This visual guide builds on the work of SAS JMP Pro 12 Response Screening Linear Fit Model Platform. Response screening is a vital component in statistical and predictive analytics. It is very important to conduct response screening to understand the effects of each factor or input variables on the response or predicted variables.

If you are working with huge datasets, response screening cut back the time it would require you to conduct other statistical tests, such as, Bivariate, ANOVA, MONOVA, etc to test the effects of each input variable to the response. SAS JMP Pro 12 has a very useful response screening platform. Your results are tabulated as well as presented in graphical forms along with all the essential statistics to make the right call.

For more information on how to conduct response screening analysis in JMP, please visit this link: Response Screening Platform.