### confusion matrix in machine learning

It would be better method for a train/test split. It is seen as a subset of artificial intelligence. A confusion matrix is a technique for summarizing the performance of a classification algorithm. Calculating a confusion matrix can give you a better idea of what your classification model is getting right and what types of errors it is making. Now, we can calculate the number of incorrect predictions for each class, organized by the predicted value. Specificity should be = TNR = TN/(TN+FP) = 4/(4+1) = 0.8. http://machinelearningmastery.com/machine-learning-performance-improvement-cheat-sheet/. Let say i have 4 class(dos, normal,worms,shellcode) then i want to make a confusion matrix where usually diagonal is true positive value. I want to comibine the result of multi-class confusion matrix thx. So for Weka's confusion matrix, the actual count is the sum of entries in a row, not a column. The confusion matrix wont help. I have two single dimensional array:one is predicted and other is expected. C1 0 0 0 0 Come write articles for us and get featured, Learn and code with the best industry experts. In the Python confusion matrix example, you pass in the expected array followed by the predictions array: results = confusion_matrix(expected, predicted). (37000 train and 2800 test images) How can I get information about which images were incorrectly predicted as a result of the confusion matrix? 0 3 8 3 444, benin malin However Sensitivity is wrongly computed as 0.06667 and Specificity is wrongly computed as 0.75. A confusion matrix summarizes the class outputs, not the images. No. In the same way, the total number of incorrect predictions for a class go into the expected row for that class value and the predicted column for that class value. What the confusion matrix is and why you need to use it. There reference data, however, does not have any instances in that category. Running this example prints the confusion matrix array summarizing the results for the contrived 2 class problem. Yes, but you would have one matrix for each fold of your cross validation. Neg Pred Value : 0.6000 https://machinelearningmastery.com/contact, i am using Weka tool and apply DecisionTable model and get following confusion matrix, Thank you for these website, i am an intern my superiors gave me some tasks about machine learning and a. and your web site helped me very well thanks a lot Jason. These resources on this website are like bare bones. In addition, even though I have Dr Jasons book Machine Learning from Scratch, I always seek ideas from this webpage. This is not always what you want: in some contexts, you mostly care about precision, and in other contexts, you really care about the recall. For example, to know the number of times the classifier confused images of 5s with 3s, you would look in the 5th row and 3rd column of the confusion matrix. men classified as women: 1 The Code Algorithms from Scratch EBook is where you'll find the Really Good stuff. my problem : malin1 malin2 malin3 malin4 benin Youtube Video: https://www.youtube.com/watch?v=4Xw19NpQCGA You need a test dataset or a validation dataset with expected outcome values. Make a prediction for each row in your test dataset. I hadnt realised that both formats are in common use. the output of confusion matrix depends on validation set?