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...
Use of the area under the ROC curve in the evaluation of machine learning algorithmsBradley, Andrew
虽然,用ROC curve来表示分类器的performance很直观好用。可是,人们总是希望能有一个数值来标志分类器的好坏。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...
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...
# Train and test the model print("Random Forest Performance:") print("Train accuracy", train_accuracy) print("Test accuracy", test_accuracy) from m2d_make_roc import create_roc_curve # import our previous ROC code # Don't worry about this lambda function. It simply reorganizes from ...
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...
The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with CO...
In this paper, we propose a new binary classification algorithm (AUCtron), based on gradient descent learning, that directly optimizes AUC (area under the ROC curve). We compare it with a linear classifier and with AUCsplit proposed. The AUCtron algorithm implicitly considers class prior probabi...
C Zhou,H Ye,F Zhang - 《Journal of Computing & Information Science in Engineering》 被引量: 20发表: 2014年 Prediction of Fault-Prone Software Modules using Statistical and Machine Learning Methods The performance of the methods is compared by computing the area under the curve using Receiver Op...
curves, Section 3 describes the esti- mators and CIs we evaluate, and Sec- tion 4 presents case studies of the es- timators and CIs in action. 2 Area Under the Precision-Recall Curve Consider a binary classification task where models produce continuous outputs, denoted Z, for each examp...