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公開日:2022/07/02 / 最終更新日:2022/07/02
Ncomputing Vspace License Crack 265
Positive Detection with Deep Learning [Jasdeep Sra] Mac OS X Journal page80 (5b) [Marius Petricevicius] 13, 2016
False positive detection is a type of error in machine learning which occurs when a computer misclassifies an instance as a member of the class of interest (here, a bird species).
When we get confused about what a computer is trying to do, it is because we are trying to understand in human terms what the computer is doing. We tend to think that we are doing the computer job, but instead, the computer is actually doing the job for us. We mistakenly think that because the computer made a mistake in assigning a class to a particular example, then we are equally likely to make a similar mistake.
For example, when we attempt to identify a bird in a picture, we can falsely detect a pheasant or a peacock as a bird. An even more common example of a false positive is when we incorrectly detect a dog as a cat.
The true/false positive ratio (TPR) in a dataset represents the fraction of examples that are correctly classified. The false positive rate (FPR) in a dataset represents the fraction of examples that are incorrectly classified.
Detecting a bird in a picture is a recognition problem (because we want to know if the bird is a bird or not). In classification, the goal is to predict the class label of the example.
Evaluation of classifiers
Classifiers are evaluated using one of two standard methods:
ROC curves are useful when we have no idea about the true class labels of the examples. When the class labels are known, we can use a standard evaluation metric, such as the area under the ROC curve (AUC).
When we use the ROC curve method, we plot the TPR and the FPR as function of the threshold for the classifier’s output. The TPR is a function of the threshold because a higher threshold corresponds to a smaller set of examples that will be predicted as a member of a particular class. The FPR is a function of the threshold because a lower threshold will predict more examples as a member of the other class.
The ROC curve method can be interpreted in terms of a human observer: The curve plots the fraction of examples that are correctly identified (true positives) and the fraction of examples that are incorrectly identified (false positives). When the human observer is faced with the same task as the machine, the curve plots the fraction of examples that are correctly classified (true
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