In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine l
1. AUC (Area Under Curve) 被定义为ROC曲线下的面积,取值范围一般在0.5和1之间。 使用AUC值作为评价标准是因为很多时候ROC曲线并不能清晰的说明哪个分类器的效果更好,而作为一个数值,对应AUC更大的分类器效果更好。 2.AUC 的计算方法 非参数法:(两种方法实际证明是一致的) 梯形法则:早期由于测试样本有限,我们...
于是Area Under roc Curve(AUC)就出现了。顾名思义,AUC的值就是处于ROC curve下方的那部分面积的大小。通常,AUC的值介于0.5到1.0之间,较大的AUC代表了较好的performance。好了,到此为止,所有的 前续介绍部分结束,下面进入本篇帖子的主题:AUC的计算方法总结。 一、假正例和假负例 分类器的正确率和召回率 前几...
Calculating the Area Under Curve (AUC) The AUC of a classification model is defined as the probability that the model will rank a random positive example above a random negative example. Using the confusion matrix, we can define other quantities as follows: The True Positive Rate (TPR) or se...
The area under curve (AUC) method was used for accuracy assessment. SVM and DT produced the highest accuracies of prediction, with rates of 85.52% and 88.47% respectively, using DS1 (the LiDAR dataset). Altitude, SPI and TRI were found to have a significant impact on the precision of ...
In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k...
The values of AUC (area under curve) for all five hazards using the best models are greater than 0.8, demonstrating that the model’s predictive abilities are acceptable. A machine learning approach can prove to be very useful tool for hazard management and disaster mitigation, particularly for ...
QLD 4072, Australia (Received 15 April 1996; in revisedform 29 July 1996; receivedfor publication 10 September 1996) Abstract--In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms....
The area under the precision-recall curve (AUCPR) is a single number summary of the information in the precision-recall (PR) curve. Similar to the receiver operating characteristic curve, the PR curve has its own unique properties that make estimating its enclosed area challenging. Besides a po...
In active learning, a machine learning algorithmis given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance...