Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Hinge Loss → B. Logistic Loss → C. Neyman-Pearson Loss → D. Cross-Entropy Loss
B
Neyman-Pearson Loss → A. Hinge Loss → D. Cross-Entropy Loss → C. Logistic Loss
C
Logistic Loss → D. Cross-Entropy Loss → A. Hinge Loss → B. Neyman-Pearson Loss
D
Cross-Entropy Loss → C. Logistic Loss → B. Neyman-Pearson Loss → A. Hinge Loss
Understanding the Answer
Let's break down why this is correct
Answer
The least common choice for a multi‑class problem is the Hinge loss, which was designed for binary SVMs and does not naturally handle more than two classes. Next is Logistic loss, often used for binary classification but extendable to multi‑class via one‑vs‑rest schemes. Neyman‑Pearson loss is a niche objective that controls error rates and is rarely used for general multi‑class training. The most standard and effective choice is Cross‑Entropy loss, which directly optimizes the probability of each class and is the default in most neural network frameworks. Thus the typical ordering from least to most suitable is Hinge → Logistic → Neyman‑Pearson → Cross‑Entropy.
Detailed Explanation
The sequence starts with hinge loss, then logistic loss, then Neyman-Pearson loss, and ends with cross‑entropy loss. Other options are incorrect because The idea that Neyman‑Pearson loss is the best for multi‑class comes from its use in controlling false alarms, but it does not train a probability model; Thinking logistic loss is the top choice ignores that cross‑entropy gives a sharper gradient for probability estimation.
Key Concepts
Multi-class Loss Functions
Classification Models
Optimization Techniques
Topic
Multi-class Loss Functions
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In a multi-class classification problem, you are using the softmax function to output class probabilities. If the cross-entropy loss is calculated, which of the following statements about gradient descent is true for optimizing the model parameters?
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2
Question 2In 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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3
Question 3Which of the following loss functions are suitable for evaluating the performance of multi-class classification models? Select all that apply.
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4
Question 4Arrange 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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5
Question 5Given a multi-class classification scenario with the following loss functions: 1) Cross-Entropy Loss, 2) Hinge Loss, 3) Mean Squared Error, and 4) Focal Loss, which loss function would be most appropriate for a model designed to classify images of handwritten digits with a focus on reducing the impact of misclassifying less frequent digits?
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6
Question 6When 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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