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Comparative Performance of Different Statistical Models for Predicting Ground-Level Ozone (O3) and Fine Particulate Matter (PM2.5) Concentrations in Montréal, Canada

By: Edouard Philippe Martin

Ground–level ozone (O3) and fine particulate matter (PM2.5) are two air pollutants known to reduce visibility, to have damaging effects on building materials and adverse impacts on human health. O3 is the result of a series of complex chemical reactions between nitrogen oxides (NOx) and volatile organic compounds (VOCs) in the presence of solar radiation. PM is a class of airborne contaminants composed of sulphate, nitrate, ammonium, crustal components and trace amounts of microorganisms. PM2.5 is the respirable subgroup of PM having an aerodynamic diameter of less than 2.5 μm. Development of effective forecasting models for ground-level O3 and PM2.5 is important to warn the public about potentially harmful or unhealthy concentration levels.

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Dan Steinberg On Interaction Detection With TreeNet®

Recent advances in machine learning technology make it possible to determine definitively whether or not interactions of any degree need to be included in a predictive model.

We can thus establish conclusively, for example, for a given set of predictors, that an additive model (one with no interactions) cannot be improved upon with interactions. Or alternatively, one might prove that a model with interactions will outperform a model without them.

Further, we can now identify precisely which interactions are supported by the data, and also the degree of interaction, even in very high dimensional data. The tools we use to achieve these results are extensions of Stanford University Professor Jerome Friedman's TreeNet, developed by the authors and embedded in the Salford Systems TreeNet 2.0 Pro Ex product.

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Data Mining Revolutionaries Will Speak At The Salford Analytics And Data Mining Conference

SAN DIEGO — CART® and RandomForests® co–developers include two of the prominent speakers for Salford Systems' Analytics and Data Mining Conference, which will be held in San Diego, CA May 24–25, 2012.

CART co–developer Dr. Richard Olshen's interests regarding research are in statistics and mathematics and their applications to medicine and biology. Many efforts have concerned binary tree–structured algorithms for classification, regression, survival analysis, and clustering. Those for classification and survival analysis have been used with success in computer–aided diagnosis and prognosis, especially in cardiology, oncology, and toxicology.

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Book Release: Mobile Analytics

'Mobile Analytics' written by Jesus Mena, Salford Systems client and Data Mining Consultant, a book about the modeling of mobile behaviors in millions of peoples' pockets or purses – that are incredibly powerful diaries of a person's life – continuously and intimately broadcasting where, how, when and what products, content, games, news, movies, relationships, TV shows, books, searches, sports, music, services, interests, places, entertainment, etc., consumers want.'

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