The leading vendor-neutral conference about predictive analytics is holding its seventh annual conference this October 11-12. Once again it's time for all predictive analytics smartest minds to gather and explore all the latest.
We are thrilled to announce the seventh edition of Predictive Analytics World London!
The leading vendor-neutral conference about predictive analytics is holding its seventh annual conference this October 11-12. Once again it's time for all predictive analytics smartest minds to gather and explore all the latest in the field.
A dataset with M items has 2M subsets anyone of which may be the one satisfying our objective. With a good data display and interactivity our fantastic pattern-recognition defeats this combinatorial explosion by extracting insights from the visual patterns. This is the core reason for data visualization. With parallel coordinates the search for relations in multivariate data is transformed into a 2-D pattern recognition problem. Together with criteria for good query design, we illustrate this on several real datasets (financial, process control, credit-score, one with hundreds of variables) with stunning results. A geometric classification algorithm yields the classification rule explicitly and visually. The minimal set of variables, features, are found and ordered by their predictive value. A model of a country’s economy reveals sensitivities, impact of constraints, trade-offs and economic sectors unknowingly competing for the same resources. An overview of the methodology provides foundational understanding; learning the patterns corresponding to various multivariate relations. These patterns are robust in the presence of errors and that is good news for the applications. A topology of proximity emerges opening the way for visualization in Big Data. Learn how to answer questions you did not know … how to ask.
Every analytics challenge reduces, at its technical core, to optimizing a metric. Product recommendation engines push items to maximize a customer’s purchases; fraud detection algorithms flag transactions to minimize losses; and so forth. As modeling and classification (optimization) algorithms improve over time, one could imagine obtaining a solution merely by defining the guiding metric. But are our tools that good? More importantly, are we aiming them in the right direction? I think, too often, the answer is no. I’ll argue for clear thinking about what exactly it is we ask our computer assistant to do for us, and recount some illustrative war stories. (Analytic heresy guaranteed.)
