Later, we’re trying to predict the acceptance status of a new paper represented by sample xtest; we pass the sample into each of the models to obtain the value of hθ(xtest) for each model. The table below (on the top of the next page) summarizes the pair of classes recognized by each of the 10 models, as well as the predicted value hθ(xtest) obtained by each model on the input xtest:
Consider the problem of trying to predict the acceptance status of an academic paper submitted to a given conference. We would like to use specific features to predict the acceptance status of a paper into one of the following categories [Accept, Weak Accept, Weak Reject, Reject, No Judgement].
We’ve decided to use a one-vs-one multi-class classification strategy: we train one classifier for each pair of classes. Later, we’re trying to predict the acceptance status of a new paper represented by sample xtest; we pass the sample into each of the models to obtain the value of hθ(xtest) for each model. The table below (on the top of the next page) summarizes the pair of classes recognized by each of the 10 models, as well as the predicted value hθ(xtest) obtained by each model on the input xtest:
in this question is to use the information provided to determine which of the five classes xtest belongs to. Show your working clearly e.g. show how you compute the probability of each class and how you deduce which class is the correct one. Finally, clearly indicate your final predicted class.
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