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Regression Error Characteristic Surfaces

by Luís Torgo - In Proc. of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'05), ACM , 2005
"... This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information for analyzi ..."
Abstract - Cited by 7 (1 self) - Add to MetaCart
This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information

Regression Error Characteristic Curves

by Jinbo Bi Bij~rpi. Edu, Kristin P. Bennett Bennek~rpi. Edu
"... Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualiTr ing and comparing classification results. Re-gression Error Characteristic (REC) curves generalize ROC curves to regression. REC curves plot the error tolerance on the x-axis versus the percentage of points predicte ..."
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Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualiTr ing and comparing classification results. Re-gression Error Characteristic (REC) curves generalize ROC curves to regression. REC curves plot the error tolerance on the x-axis versus the percentage of points

Tests for Symmetry of Regression Errors

by Alicia Pérez Alonso , 2003
"... In this essay we discuss how to test for symmetry of unobservable errors. Two easily computable test statistics based on the estimated regression residuals are developed. Alternatively, we consider various nonparametric procedures that have been proposed in the literature to test for symmetry of obs ..."
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In this essay we discuss how to test for symmetry of unobservable errors. Two easily computable test statistics based on the estimated regression residuals are developed. Alternatively, we consider various nonparametric procedures that have been proposed in the literature to test for symmetry

Regression Error Characteristic CurVes

by Jinbo Bi, Kristin P. Bennett - Proceedings of the 20th International Conference on Machine Learning , 2003
"... Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generalize ROC curves to regression. REC curves plot the error tolerance on the xaxis versus the percentage of points predicted wi ..."
Abstract - Cited by 35 (0 self) - Add to MetaCart
Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generalize ROC curves to regression. REC curves plot the error tolerance on the xaxis versus the percentage of points predicted

Least angle regression

by Bradley Efron, Trevor Hastie, Iain Johnstone, Robert Tibshirani , 2004
"... The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to s ..."
Abstract - Cited by 1326 (37 self) - Add to MetaCart
to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods. Three main properties are derived: (1) A simple modification of the LARS algorithm

On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes

by Andrew Y. Ng, Michael I. Jordan , 2001
"... We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is i ..."
Abstract - Cited by 520 (8 self) - Add to MetaCart
We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size

2 On Ranks of Regression Errors and Residuals

by unknown authors , 2012
"... ar ..."
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Abstract not found

Estimating Nonlinear Regression Errors without Doing Regression

by Hong Pi, Carsten Peterson , 1994
"... : A method for estimating nonlinear regression errors and their distributions without performing regression is presented. Assuming continuity of the modeling function the variance is given in terms of conditional probabilities extracted from the data. For N data points the computational demand is N ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
: A method for estimating nonlinear regression errors and their distributions without performing regression is presented. Assuming continuity of the modeling function the variance is given in terms of conditional probabilities extracted from the data. For N data points the computational demand

CoREBench: Studying Complexity of Regression Errors

by Marcel Böhme, Abhik Roychoudhury
"... Intuitively we know, some software errors are more complex than others. If the error can be fixed by changing one faulty statement, it is a simple error. The more substantial the fix must be, the more complex we consider the error. In this work, we formally define and quantify the com-plexity of an ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
-plexity of an error w.r.t. the complexity of the error’s least complex, correct fix. As a concrete measure of complexity for such fixes, we introduce Cyclomatic Change Complexity which is inspired by existing program complexity metrics. Moreover, we introduce CoREBench, a collection of 70 regression errors

Research Track Poster Regression Error Characteristic Surfaces

by Luís Torgo
"... This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information for analyzi ..."
Abstract - Add to MetaCart
This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information
Results 1 - 10 of 8,078