Results 1  10
of
8,078
Regression Error Characteristic Surfaces
 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
"... Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualiTr ing 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 predicte ..."
Abstract
 Add to MetaCart
Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualiTr ing 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
Tests for Symmetry of Regression Errors
, 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 ..."
Abstract
 Add to MetaCart
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
 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
, 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
, 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
Estimating Nonlinear Regression Errors without Doing Regression
, 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
"... 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 complexity 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
"... 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