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.

Which of the following is true about the softmax function in the context of multinomial logistic regression?单项选择题

类似问题

Question at position 19 Which of the following functions can be used as an activation function in the output layer of a neural network if we wish to predict the probabilities of n classes (p1, p2,...,pn) such that sum of p over all n equals to 1?ReLuSigmoidTanhSoftmax

Softmax outputs independent probabilities for each class, so multiple classes can simultaneously receive high probability (e.g., 0.9 and 0.9) for the same input.

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

Question at position 8 An excess of growth hormone (GH) before puberty can result in the development ofgigantism.Graves' disease.acromegaly.pituitary dwarfism.Clear my selection

Question at position 6 The most abundant supporting cells in the central nervous system are called Question Blank 1 of 1[input].

Question at position 5 In the eye, the lens functions tofocus light on the retina at the back of the eye.protect the surface of the eye.increase vision in low-light conditions.move the eye side to side.Clear my selection

更多留学生实用工具

智能学习助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单