What is a disadvantage of one-vs-all classification?
It cannot output probability estimates of classes.
It requires more models to be created compared to one-vs-one.
It does not handle two-class classification well.
There’s an ambiguous region where multiple classes are valid outputs.
The Correct Answer and Explanation is:
Correct Answer:
It requires more models to be created compared to one-vs-one.
Detailed Explanation (300+ words):
In multi-class classification problems, two popular strategies are one-vs-all (OvA) and one-vs-one (OvO). These approaches are used when the base classifier (e.g., logistic regression or SVM) is inherently binary—meaning it can only distinguish between two classes.
One-vs-all (OvA) classification works by creating one binary classifier for each class. Each classifier learns to distinguish one class against all the other classes. For example, if there are N classes, OvA will train N models. Each model outputs a score or probability indicating how likely a sample belongs to its specific class. The final predicted class is the one whose classifier outputs the highest score.
Now let’s analyze the options:
- “It cannot output probability estimates of classes” is incorrect. Many implementations of OvA (especially those using logistic regression or neural networks) can output class probabilities based on the model’s score or softmax function.
- “It requires more models to be created compared to one-vs-one” is incorrect, and thus not the right answer. OvO requires N(N – 1)/2 classifiers, which is significantly more than N classifiers used in OvA. So OvA requires fewer, not more, models.
- “It does not handle two-class classification well” is incorrect because OvA is not even needed for two-class problems—you simply use a single binary classifier. So this is not a disadvantage.
- “There’s an ambiguous region where multiple classes are valid outputs” is correct. In OvA, it’s possible that more than one classifier outputs a strong positive response, or sometimes none do. This overlap or ambiguity creates a challenge in decision-making, especially when classifiers are poorly calibrated. For instance, if two classifiers output similarly high confidence scores, the model may become indecisive. This is a key disadvantage of OvA.
Corrected Final Answer:
✅ There’s an ambiguous region where multiple classes are valid output