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Related Questions
- How do different classification objectives, such as binary cross-entropy and multiclass cross-entropy, affect the model's output in terms of probability distributions versus discrete labels?
- Can you explain how the choice of classification objective influences the model's interpretation of the output as probabilities versus class labels?
- What are the implications of using a classification objective that encourages the model to output probabilities versus one that outputs discrete labels?
- How does the difference between probability distributions and discrete labels impact the model's performance in terms of metrics such as accuracy and AUC-ROC?
- Can you provide an example of a scenario where a model outputs probabilities that are not meaningful, but the output of discrete labels is useful?
- How do classification objectives influence the model's ability to handle uncertainty and ambiguity in the input data?
- What are some strategies for selecting the appropriate classification objective for a given problem, considering factors such as the number of classes and the distribution of the target variable?
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