So, I think that each classifier will erroneously choose bad support vectors, but as they won't make the same error, each classifier result can't balance between each other and cancel the error. verb If your eyes rest on a particular person or object, you look directly at them, rather than somewhere else. Synonyms: support, stand, base, holder More Synonyms of rest 9. When you are sitting, keep your elbow on the arm rest. They won't "suffer" from the same imbalance problem in my opinion, because for one 'one vs rest classifier', the classifier will erroneously choose support vectors based on a bad C penalty for the positive class, and for another 'one vs rest classifier', it will make another error in the choice of support vectors, but not the same error as the previous classifier as each class is different. A rest is an object that is used to support something, especially your head, arms, or feet. For each classifier, the class is fitted against all the other classes. The strategy consists in fitting one classifier per class. If you don't balance your classes, you're not building a good classifier as you ignore too much the 'ones' (rare positive samples), even if you just look as the classification score and not the classification result (class + or class -). The one-vs-rest strategy, also known as one-vs-all, is implemented in OneVsRestClassifier. So, if you don't want your negative samples to "overtake" the positive ones during the classification, you have to put a different weight on each class. For example, in a SVM you cannot find a good C penalty if you have unbalanced classes (see this visual example). On this page youll find 407 synonyms, antonyms, and words related to rest, such as: vacation, break, breather, calm, calmness, and cessation. Metacademy is a great resource which compiles lesson plans on popular machine learning topics.įor Beginner questions please try /r/LearnMachineLearning, /r/MLQuestions or įor career related questions, visit /r/cscareerquestions/Īs long as the k classes are balanced to begin with, then all k one-vs-rest classifiers equally "suffer" from the same imbalance problem, so no class has an advantage (or disadvantage). Synonyms for ONE AND ALL: all, everyone, everybody, anyone, someone, anybody, somebody, each and everyone Antonyms of ONE AND ALL: none, nobody, no one. Please have a look at our FAQ and Link-Collection As long as the k classes are balanced to begin with, then all k one-vs-rest classifiers equally 'suffer' from the same imbalance problem, so no class has an advantage (or disadvantage). Rules For Posts + Research + Discussion + Project + News on Twitter Chat with us on Slack Beginners: The one class in every one-vs-rest classifier doesn't necessarily need to beat the rest to be chosen, it needs to beat all the other one-vs-rest classifiers.
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