##### Department of Mathematics,

University of California San Diego

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### Statistics Seminar

## Xin Tong

#### Marshall School of Business, University of Southern California

## Neyman-Pearson classification

##### Abstract:

In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (that is, the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (that is, the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, alpha, on the type I error. Although the NP paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than alpha do not satisfy the type I error control objective because the resulting classifiers are still likely to have type I errors much larger than alpha. This talk introduces the speaker's work on NP classification algorithms and their applications and raises current challenges under the NP paradigm.

Host: Jelena Bradic

### October 5, 2018

### 10:00 AM

### AP&M 7321

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