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PREDICTIVE MODELING WITH BIG DATA:Is Bigger Really Better

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With the increasingly widespread collection and processing of ‘‘big data,’’ there is natural interest in using these
data assets to improve decision making. One of the best understood ways to use data to improve decision making is via predictive analytics

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ABSTRACT
With the increasingly widespread collection and processing of ‘‘big data,’’ there is natural interest in using these
data assets to improve decision making. One of the best understood ways to use data to improve decision making is
via predictive analytics. An important, open question is: to what extent do larger data actually lead to better
predictive models? In this article we empirically demonstrate that when predictive models are built from sparse,
fine-grained data—such as data on low-level human behavior—we continue to see marginal increases in predictive
performance even to very large scale. The empirical results are based on data drawn from nine different
predictive modeling applications, from book reviews to banking transactions. This study provides a clear illustration
that larger data indeed can be more valuable assets for predictive analytics. This implies that institutions
with larger data assets—plus the skill to take advantage of them—potentially can obtain substantial competitive
advantage over institutions without such access or skill. Moreover, the results suggest that it is worthwhile for
companies with access to such fine-grained data, in the context of a key predictive task, to gather both more data
instances and more possible data features. As an additional contribution, we introduce an implementation of the
multivariate Bernoulli Naı¨ve Bayes algorithm that can scale to massive, sparse data.

INTRODUCTION
One of the exciting opportunities presented by the proliferation of big data architectures is the ability to conduct
predictive analytics based on massive data. However, an important and open question is whether and when massive
data actually will improve predictive modeling. Authors have argued for decades for the need to scale up predictive modeling algorithms to massive data.1,2 However, there is surprisingly scant empirical evidence supporting continued scaling up to modern conceptions of ‘‘massive data’’

Year : 2013

By : Enric Junque de Fortuny , David Martens and Foster Provost
File Information : English Language /12 Page / Size : 645 K

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سال : 2013

کاری از :Enric Junque de Fortuny , David Martens and Foster Provost

اطلاعات فایل : زبان انگلیسی /12 صفحه /حجم : 645 K

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