Error Classification Rate
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Classification Error Rate Wiki
the top How is the “classification error rate” of an artificial neural network calculated? up vote 0 down vote favorite Frequently I see artificial neural networks compared by their "classification error rates" or "error rates", particularly for multi-class problems like CIFAR-10. What does this error rate actually refer to? Hamming loss? How is it calculated? neural-networks performance share|improve this question asked Jul 28 '14 at 0:34 gavinmh 178311 add a comment| 2 Answers 2 classification error rate wikipedia active oldest votes up vote 1 down vote The error rate of any classifier is typically the proportion of classifications it gets wrong, i.e., the input is class A and the classifier determines that it's class B, B != A. share|improve this answer answered Jul 28 '14 at 1:12 JeffM 29113 add a comment| up vote 1 down vote You count the number of datums where the output neuron corresponding to the true class is highest of all outputs of the softmax activation function. The proportion of that number to the total number of data is the classification rate. $100\%$ minus the value results in the error rate. share|improve this answer answered Aug 12 '14 at 12:45 Angelorf 495319 add a comment| Your Answer draft saved draft discarded Sign up or log in Sign up using Google Sign up using Facebook Sign up using Email and Password Post as a guest Name Email Post as a guest Name Email discard By posting your answer, you agree to the privacy policy and terms of service. Not the answer you're looking for? Browse other questions tagged neural-networks performance or ask your own question. asked 2 years ago viewed 1840 times active 1 year ago Related 2Is my interpretation of Neural Network results correct?0Reported error rates on neural networks2One or two output neurons
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Minimum Error Rate Classification
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Classification Error Rate Formula
Community Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up How to calculate classification error rate up vote http://stats.stackexchange.com/questions/109630/how-is-the-classification-error-rate-of-an-artificial-neural-network-calculate 1 down vote favorite Alright. Now this question is pretty hard. I am going to give you an example. Now the left numbers are my algorithm classification and the right numbers are the original class numbers 177 86 177 86 177 86 177 86 177 86 177 86 177 86 177 86 177 86 177 89 177 89 177 89 177 89 177 89 177 89 177 89 http://stackoverflow.com/questions/10067118/how-to-calculate-classification-error-rate So here my algorithm merged 2 different classes into 1. As you can see it merged class 86 and 89 into one class. So what would be the error at the above example ? Or here another example 203 7 203 7 203 7 203 7 16 7 203 7 17 7 16 7 203 7 At the above example left numbers are my algorithm classification and the right numbers are original class ids. As can be seen above it miss classified 3 products (i am classifying same commercial products). So at this example what would be the error rate? How would you calculate. This question is pretty hard and complex. We have finished the classification but we are not able to find correct algorithm for calculating success rate :D algorithm cluster-analysis classification ratio confusion-matrix share|improve this question edited May 11 '13 at 16:26 denis 10.6k43855 asked Apr 8 '12 at 22:36 MonsterMMORPG 6,17641121222 add a comment| 4 Answers 4 active oldest votes up vote 2 down vote accepted Here's a longish example, a real confuson matrix with 10 input classes "0" - "9" (handwritten digits), and 10 output clusters labelled A - J. Confusion matrix for 5620 optdigits: T
Mining|Data and Knowledge Discovery|Pattern Recognition|Data Science|Data Analysis) Statistics Learning - (Error|misclassification) Rate - false (positives|negatives) Statistics Learning - (Error|misclassification) http://gerardnico.com/wiki/data_mining/error_rate Rate - false (positives|negatives) Table of Contents 1 - About 2 - Articles Related 3 - Rate 3.1 - False 3.1.1 - False Positive 3.1.2 - False Negative 3.2 - True 3.2.1 - True Positive 4 - How to decrease it 5 - Others Metrics 5.1 - Sensitivity error rate / Specificity 5.1.1 - Sensitivity 5.1.2 - Specificity 5.2 - Lift , Precision, Recall 1 - About The error rate is a prediction error metrics for a binary classification problem. The error rate metrics for a two-class classification problem are calculated with the help of a Confusion Matrix. classification error rate The below confusion matrix shows the results for a two-class classification problem where the target can take the value: Positive Or Negative True = Truth = Good Predictions 2 - Articles Related Data Mining - (Parameters|Model) (Accuracy|Precision|Fit|Performance) MetricsData Mining - (Anomaly|outlier) DetectionStatistics - (Base rate fallacy|Bonferroni's principle)Machine Learning - Confusion Matrix(Statistics|Data Mining) - (K-Fold) Cross-validation (rotation estimation)Statistics Learning - Prediction Error (Training versus Test)Statistics - ROC Plot and Area under the curve (AUC)Statistics - (Threshold|Cut-off) of binary classificationStatistical Learning - Two-fold validation 3 - Rate False rate are not desired while true rate are. For instance, in a spam application, a false negative will deliver a spam in your inbox and a false positive will deliver legitimate mail to the junk folder. 3.1 - False 3.1.1 - False Positive False Positive (also known as false alarm) are predictions that should be fals