roc curve machine learning

Machine Learning: ROC curve of classification algorithm performance indicators and performance indicator roc Before introducing the ROC curve, let's talk about the confusion matrix and

transformed, the ROC curve can remain unchanged. In the actual data set, the sample class imbalance often occurs, that is, the positive and negative sample ratio is large, and the positive and negative samples in the test data may change over time. Is the contrast between the ROC curve and the Presision-recall curve:I

of the portion of the area below the ROC curve. In general, the value of AUC is between 0.5 and 1.0, and the larger AUC represents a better performance. Well, so far, all of the previous introductory sections are over, and the following is the topic of this post: a summary of the calculation methods of the AUC. Most intuitively, according to the AUC name, we know that the area below the

Keywords: PR curve, ROC curve, Machine Learning, image processingTo help you understand, for example, we need to detect a person in an image, the classifier divides each pixel on the image into human and non-pixel, the target is a person, so the detection of human pixels wit

Reprinted please indicate Source Address: http://www.cnblogs.com/xbinworld/archive/2013/04/21/3034300.html Pattern Recognition and machine learning (PRML) book learning, Chapter 1.1, introduces polynomial curve fitting) The doctor is almost finished. He will graduate next year and start preparing for graduation

Original writing. For more information, see http://blog.csdn.net/xbinworld,bincolumns. Pattern Recognition and machine learning (PRML) book learning, Chapter 1.1, introduces polynomial curve fitting) The doctor is almost finished. He will graduate next year and start preparing for graduation this year. He feels that he

sixth week. Design of learning curve and machine learning system Learning Curve and machine learning System Design Key Words

to the right in this image. We can generally see the two learning curves, the two curves of blue and red are approaching each other. Therefore, if we extend the curve to the right, it seems that the training set error is likely to increase gradually. The cross-validation set error will continue to decline. Of course, we are most concerned with cross-validation set errors or test set errors. So from this pi

This article describes how to use Python to draw the ROC curve and calculate the AUC value. if you need it, let's take a look at it. Preface The ROC curve and AUC are often used to evaluate the merits of a binary classifier. This article will first briefly introduce ROC an

Preface The ROC (Receiver Operating characteristic) curve and AUC are often used to evaluate the merits and demerits of a binary classifier (binary classifier). This article will start with a brief introduction of ROC and AUC, and then use an example to demonstrate how Python makes the ROC

Using Python to draw ROC curve and AUC value calculation, rocauc Preface The ROC curve and AUC are often used to evaluate the merits of a binary classifier. This article will first briefly introduce ROC and AUC, and then use an example to demonstrate how to create a

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

don't know what that means. ]What is the difference between the first order and the Hi Jiezheng? What are the occasions for each?The biggest difference between the two is whether the feature coefficient will be 0, first-order penalty can not only reduce the complexity of the model, but also to complete the feature screening, that is, the coefficient of the partial feature is reduced to 0, the second penalty may reduce the coefficient of some features to a small, but generally will not reduce th

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

,,. Continue, at 0.7, it is considered that the >=0.7 is a positive sample and the , . ... And so on, you can calculate all the FPR, TPR, according to these points to draw a ROC curve. When the number of samples is large enough, the ROC curve becomes smoother. Calculate the area of each "small rectangl

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduc

Source: https://www.cnblogs.com/jianxinzhou/p/4083921.html1. The problem of overfitting (1) Let's look at the example of predicting house price. We will first perform linear regression on the data, that is, the first graph on the left. If we do this, we can obtain such a straight line that fits the data, but in fact this is not a good model. Let's look at the data. Obviously, as the area of the house increases, the changes in the housing price tend to be stable, or the more you move to the right

Dr. Hangyuan Li's "Talking about my understanding of machine learning" machine learning and natural language processing [Date: 2015-01-14] Source: Sina Weibo Hangyuan Li [Font: Big Small] Calculating time, from the beginning to the present, do m