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Can a classifier that never says “yes” be useful?

Many data science projects and presentations are needlessly derailed by not having set shared business relevant quantitative expectations early on (for some advice see Setting expectations in data science projects). One of the most common issues is the common layman expectation of “perfect prediction” from classification projects. It is important to set expectations correctly so your partners know what you are actually working towards and do not consider late choices of criteria disappointments or “venue shopping.” Continue reading Can a classifier that never says “yes” be useful?

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Importance Sampling

We describe briefly the powerful simulation technique known as “importance sampling.” Importance sampling is a technique that allows you to use numerical simulation to explore events that, at first look, appear too rare to be reliably approximated numerically. The correctness of importance sampling follows almost immediately from the definition of a change of density. Like most mathematical techniques, importance sampling brings in its own concerns and controls that were not obvious in the original problem. To deal with these concerns (like picking the re-weighting to use) we will largely appeal to the ideas from “A Tutorial on the Cross-Entropy Method” Pieter-Tjerk de Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein, Annals of Operations Research, 2005 vol. 134 (1) pp. 19-67. Continue reading Importance Sampling