A predictive model has an error margin that should be taken into account when predicting the outcome of a given event based on that model. Even a state-of-the art analytics package cannot predict with 100 percent accurary the outcome of an test instance whose parameters (features) may vary depending on the test environment.
To me there's a big difference between the Super Bowl and the playoffs, and the Presidential election: the first is determined by the participants, and the second by the audience, which is polled. It's not like the views of the audience have any determination on the final result; it's totally about the performance of the two teams involved.
So there's a lot of factors involved -- weather, injuries, who's playing, etc. For that reason, while it's mildly interesting to me that Nate failed to correctly calculate the victors of the playoffs, doing so correctly doesn't impress me nearly as much as his performance during the election season.
My gambler boyfriend observes that if Nate could indeed correctly calculate the winners of the games, he'd end up making a lot more money placing bets on them than he could make at the New York Times, which caused me to wonder (intellectually) what effect deliberately throwing the predictions could have on the odds of the games -- though it is of course illegal to bet on football games outside of Las Vegas and no doubt his New York Times contract forbids it anyway.
"He wants to shout at them to check if all the predictions they have made in the past came to be. "
So is it only on this basis that Mr. Taleb will believe in predictions? Well that scenario is the lofty ambition of analytics. That's were we are heading wioth this exciting but intriguing world of Analytics. Are we ever going to reach that lofty goal of been able to see all our predictions come true? I am afraid notbecause Analytics does not purport to be an exact science. However, it does gives us plenty of resources to make predictions look like a scientific process. It can only gets better with time.
"We should neither predict nor believe in predictions."
How can you say we should neither predict nor believe in predictions? Prediction itself is as old as the human race. On an individual level, everyoneone at some point in time wantred to know the future. It's just a part of human nature. We've seen astrology becoming in creasingly popular as more and more people become super curious to know the future.
From a business perspective, we are hearing a lot about forcast. I don't think there is a single business that does not do with forcasts. Over the years, forcasting has been predominatly a guessing game. However, with the advent of analytics, we are now seeing attempts to minimize the guessing aspect of forcasting. With analytics, we can now apply scientific principles to determine with some degree of certaintythe future outcomes of business decisions.
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In the fall of 2011, around 160,000 students in 190 countries enrolled in a Stanford-sponsored online course about artificial intelligence. About 23,000 completed the course and got certificates, including 248 who got a perfect score. The university offered the same course the old-fashioned way to students sitting in Stanford classrooms. None of the those students got a perfect score.
As Mitch Wagner discussed today, Yahoo is acquiring Tumblr. The big Internet debate at the moment is whether Tumblr will be good or bad for Yahoo. Regardless of their stances on the future of Yahoo itself, many claim that Yahoo will somehow ruin Tumblr.
The whole Amazon.reader debate is a double-stupid. It's stupid to think that there's any e-book buyer who doesn't know Amazon's URL, and it was stupider to let ICANN launch the whole free-form TLD initiative to start with.
Enterprises would like to move to cloud computing but are hesitant because they are concerned about providers’ ability to secure company data. Here are some tips that help to ensure that if breaches occur, the business is not left holding the bag.
Edmunds separates customers into segments based on the info it collects on its site and from partners, and uses that to push out custom content, said Brian Baron, director of business analytics for Edmunds.com, at Predictive Analytics Innovation Summit.
The automotive website uses propensity modeling to target ads and customer registration forms, said Brian Baron, director of business analytics for Edmunds.com, at Predictive Analytics Innovation Summit.
Ushering in a new era of cognitive computing systems, IBM announced today the IBM Watson Engagement Advisor, a technology breakthrough that allows brands to crunch big data in record time to transform the way they engage clients in key functions such as customer service, marketing, and sales.
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