Which of the following is true about the softmax function in the context of multinomial logistic regression?

Question

BlackTom AI · Accepted Answer

Question restatement: What is true about the softmax function in the context of multinomial logistic regression?

Option evaluation:
- Since the provided input does not include any answer options for this question, there is nothing to evaluate against a list of choices. In a typical multiple-choice setup, we would examine each option for its correctness and explain why it is right or wrong. Here, we only have the one listed answer in the 'answer' field, and no separate options to analyze.

About the concept implied by the provided answer: 'It generalizes the logistic function to handle more than two classes.'
- Why this statement is true: In binary logistic regression, the model uses the logistic (sigmoid) function to map a linear score to a probability for a single target class vs. the rest. Multinomial logistic regression extends this idea to multi-class classification by using the softmax function to model the probability distribution over K classes. The softmax takes a vector of class scores and converts them into a probability distribution over all classes, ensuring that the probabilities sum to 1. This is the natural generalization of the logistic function from two classes to K > 2 classes, where each class probability depends on all class scores and is computed as exp(score_k) / sum_j exp(score_j).

- Possible misinterpretations to avoid: Some might think softmax is only a multi-class wrapper around a one-vs-rest approach; in reality, softmax can be used directly to model a single multinomial distribution across all classes, though one-vs-rest approaches are an alternative in practice. Others might confuse softmax with a simple normalization; while softmax does normalize, its key feature is the exponentiation of scores, which amplifies differences between scores and yields a well-behaved probability distribution over multiple classes.

Summary of key concept:
- The softmax function is used in multinomial logistic regression to extend binary logistic regression to multi-class problems.
- It converts a vector of class scores into a probability distribution over all classes, enabling multi-class classification where the sum of probabilities across classes equals 1.
- This matches the intuitive notion that softmax generalizes the idea of the logistic function to more than two outcomes, rather than serving as a simple normalization or a one-vs-rest scheme by itself.

COMP90049_2025_SM2 Lecture 8 Gradient Descent & Logistic Regression Practice Quiz

Which of the following is true about the softmax function in the context of multinomial logistic regression?

View Explanation

Log in for full answers

Similar Questions

Which of the following is TRUE about softmax (mark all that apply).

In a consumer society, many adults channel creativity into buying things

Economic stress and unpredictable times have resulted in a booming industry for self-help products

People born without creativity never can develop it

A product has a selling price of $20, a contribution margin ratio of 40% and fixed cost of $120,000. To make a profit of $30,000. The number of units that must be sold is: Type the number without $ and a comma. Eg: 20000

Which of the following statement regarding cost is correct:

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler