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Loss functions for binary classifica...

Shen, Yi.

Loss functions for binary classification and class probability estimation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Loss functions for binary classification and class probability estimation.
Author:	Shen, Yi.
Description:	115 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3215.
Notes:	Supervisor: Andreas Buja.
Contained By:	Dissertation Abstracts International66-06B.
Subject:	Statistics.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3179814
ISBN:	0542200554

Loss functions for binary classification and class probability estimation.
Shen, Yi.

Loss functions for binary classification and class probability estimation. - 115 p.

Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3215.

Thesis (Ph.D.)--University of Pennsylvania, 2005.

Proper scoring rules are fully characterized by weight functions o(eta) on class probabilities eta = P[Y = 1]. These weight functions give immediate practical insight into loss functions: high mass of o(eta) points to the class probabilities eta where the proper scoring rule strives for greatest accuracy. For example, both log-loss and boosting loss have poles near zero and one, hence rely on extreme probabilities.

ISBN: 0542200554Subjects--Topical Terms:

182057
Statistics.

Loss functions for binary classification and class probability estimation.
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245 1 0 $a Loss functions for binary classification and class probability estimation.
300 $a 115 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3215.
500 $a Supervisor: Andreas Buja.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2005.
520 # $a Proper scoring rules are fully characterized by weight functions o(eta) on class probabilities eta = P[Y = 1]. These weight functions give immediate practical insight into loss functions: high mass of o(eta) points to the class probabilities eta where the proper scoring rule strives for greatest accuracy. For example, both log-loss and boosting loss have poles near zero and one, hence rely on extreme probabilities.
520 # $a We illustrate "tailoring" with artificial and real datasets both for linear models and for non-parametric models based on trees, and compare it with traditional linear logistic regression and one recent version of boosting, called "LogitBoost".
520 # $a We show that the freedom of choice among proper scoring rules can be exploited ploited when the two types of misclassification have different costs: one can choose proper scoring rules that focus on the cost c of class 0 misclassification by concentrating o(eta) near c. We also show that cost-weighting uncalibrated loss functions can achieve tailoring. "Tailoring" is often beneficial for classical linear models, whereas non-parametric boosting models show fewer benefits.
520 # $a What are the natural loss functions for binary class probability estimation? This question has a simple answer: so-called "proper scoring rules". These loss functions, known from subjective probability, measure the discrepancy between true probabilities and estimates thereof. They comprise all commonly used loss functions: lob loss, squared error loss, boosting loss (which we derive from boosting's exponential loss), and cost-weighted misclassification losses. We also introduce a larger class of possibly uncalibrated loss functions that can be calibrated with a link function. An example is exponential loss, which is related to boosting.
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