A data scientist is developing a model to classify product images into 150 distinct categories. The chosen base algorithm is a Support Vector Machine (SVM), which is inherently a binary classifier. The development team is operating under significant computational resource constraints, making training time a primary concern. Which multiclass classification strategy is the most appropriate choice for adapting the SVM model in this scenario?
The correct answer is One-vs-Rest (OvR). In a multiclass classification problem with 'K' classes, the OvR strategy trains 'K' individual binary classifiers. Each classifier is trained to distinguish one class from the remaining 'K-1' classes. For this scenario with 150 classes, OvR would require training 150 SVM models. The One-vs-One (OvO) strategy trains a binary classifier for every pair of classes, resulting in K*(K-1)/2 classifiers. For 150 classes, this would be 150*149/2 = 11,175 classifiers, which is far more computationally expensive to train than OvR. While each OvO classifier is trained on a smaller subset of data, the sheer number of classifiers makes it less efficient for problems with a large number of classes. Multinomial Logistic Regression is an inherently multiclass algorithm and is not a strategy for adapting a binary classifier like SVM. Error-Correcting Output Codes (ECOC) is a more complex ensemble method that can be more robust but is generally more computationally intensive to train than OvR, as it often involves training more than 'K' classifiers.
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Why is One-vs-Rest (OvR) preferred over One-vs-One (OvO) in this scenario?
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How does a binary classifier like SVM handle multiclass problems using the OvR approach?
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What are the trade-offs of using One-vs-Rest (OvR) for multiclass classification?