Regression Error Characteristic Curves
نویسندگان
چکیده
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 within the tolerance on the y-axis. The resulting curve estimates the cumulative distribution function of the error. The REC curve visually presents commonly-used statistics. The area-over-the-curve (AOC) is a biased estimate of the expected error. The R value can be estimated using the ratio of the AOC for a given model to the AOC for the null model. Users can quickly assess the relative merits of many regression functions by examining the relative position of their REC curves. The shape of the curve reveals additional information that can be used to guide modeling.
منابع مشابه
Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networks
Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach for model selection in ensembles of Neural Networks, in which we propose the use of REC curves in order to sele...
متن کاملBootstrap Confidence Intervals for Regression Error Characteristic Curves Evaluating the Prediction Error of Software Cost Estimation Models
The importance of Software Cost Estimation at the early stages of the development life cycle is clearly portrayed by the utilization of several algorithmic and artificial intelligence models and methods, appeared so far in the literature. Despite the several comparison studies, there seems to be a discrepancy in choosing the best prediction technique between them. Additionally, the large variat...
متن کاملBoosting for Regression Using Regression Error Characteristic Curves
Boosting is one of the most popular methods for constructing ensembles. The objective of this work is to present a boosting algorithm for regression based on the Regressor-Boosting algorithm, in which we propose the use of REC curves in order to select a good threshold value, so that only residuals greater than that value are considered as errors. The algorithm was empirically evaluated and its...
متن کاملRegression Error Characteristic Optimisation of Non-Linear Models
In this chapter recent research in the area of multi-objective optimisation of regression models is presented and combined. Evolutionary multi-objective optimisation techniques are described for training a population of regression models to optimise the recently defined Regression Error Characteristic Curves (REC). A method which meaningfully compares across regressors and against benchmark mod...
متن کاملAN ALGORITHM FOR FINDING THE EIGENPAIRS OF A SYMMETRIC MATRIX
The purpose of this paper is to show that ideas and techniques of the homotopy continuation method can be used to find the complete set of eigenpairs of a symmetric matrix. The homotopy defined by Chow, Mallet- Paret and York [I] may be used to solve this problem with 2""-n curves diverging to infinity which for large n causes a great inefficiency. M. Chu 121 introduced a homotopy equation...
متن کامل