In multi-class classification, the primary objective of using multi-class loss functions is to evaluate the model's performance by penalizing incorrect predictions through various mechanisms, such as ______ loss, which is particularly effective in optimizing probabilistic outputs.

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The missing term is **cross‑entropy** loss. Cross‑entropy measures how far a predicted probability distribution is from the true label distribution, giving a large penalty when the model assigns low probability to the correct class. During training the loss gradient pushes the predicted probabilities toward 1 for the true class and toward 0 for others, which improves the model’s confidence. For example, if a model predicts 0. 8 for class A, 0.

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The chosen loss uses the logarithm of predicted probabilities, so it strongly penalizes wrong guesses and rewards correct ones. Other options are incorrect because Some think squaring the error helps, but this loss treats all errors the same regardless of probability; The idea that taking the absolute difference works is a misconception.

Key Concepts

Multi-class loss functions

Model performance evaluation

Probabilistic outputs

Topic

Multi-class Loss Functions

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easy level question

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Practice Similar Questions

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Question 1

If a multi-class classification model consistently yields high accuracy but performs poorly on a specific underrepresented class, what underlying issue might this indicate about the loss function used?

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Question 2

In multi-class classification, which loss function is best suited for optimizing the separation between classes while allowing for margin-based errors?

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Question 3

Which of the following loss functions are suitable for evaluating the performance of multi-class classification models? Select all that apply.

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Question 4

In a multi-class classification scenario, which loss function is best suited for maximizing the margin between classes while allowing some misclassifications?

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Question 5

Arrange the following steps in the correct order for evaluating a multi-class classification model using loss functions and metrics: A) Select appropriate loss function, B) Train the model, C) Evaluate model performance using classification metrics, D) Adjust model parameters based on evaluation results.

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Question 6

When selecting a loss function for a multi-class classification task, which factor is most crucial for ensuring model performance?

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Question 7

When selecting a loss function for a multi-class classification problem, which of the following considerations is most critical for aligning model performance with classification objectives?

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