HomeQuestionsComputer ScienceMulti-class Loss Functions
📚 Learning Guide
Multi-class Loss Functions
medium

Which of the following loss functions would be most appropriate for a multi-class classification problem where the goal is to maximize the margin between classes?

Master this concept with our detailed explanation and step-by-step learning approach

Learning Path
Learning Path

Question & Answer
1
Understand Question
2
Review Options
3
Learn Explanation
4
Explore Topic

Choose AnswerChoose the Best Answer

A

Hinge Loss

B

Neyman-Pearson Loss

C

Logistic Loss

D

Squared Error Loss

Understanding the Answer

Let's break down why this is correct

That loss pushes each class away from the others. Other options are incorrect because Neyman-Pearson Loss focuses on balancing false positives and negatives; Logistic loss gives probabilities.

Key Concepts

Multi-class Loss Functions
Margin Maximization
Classification Models
Topic

Multi-class Loss Functions

Difficulty

medium level question

Cognitive Level

understand

Deep Dive: Multi-class Loss Functions

Master the fundamentals

Definition
Definition

Multi-class loss functions are designed to evaluate the performance of multi-class classification models by penalizing incorrect predictions. They include Neyman-Pearson loss, hinge loss, and logistic loss, each serving different optimization and evaluation purposes.

Topic Definition

Multi-class loss functions are designed to evaluate the performance of multi-class classification models by penalizing incorrect predictions. They include Neyman-Pearson loss, hinge loss, and logistic loss, each serving different optimization and evaluation purposes.

Ready to Master More Topics?

Join thousands of students using Seekh's interactive learning platform to excel in their studies with personalized practice and detailed explanations.

Start Learning with SeekhExplore More Multi-class Loss Functions Questions