Classification problems involve using only categorical inputs to predict a categorical output.

Question

BlackTom AI · Accepted Answer

The statement claims that classification problems involve using only categorical inputs to predict a categorical output. This is not accurate.

First, consider the possibility that the inputs could be numerical as well as categorical. Many classification tasks use a mix of feature types, such as age (numerical), income (numerical), and category labels (categorical). Algorithms like logistic regression, random forests, gradient boosting, and neural networks can handle both numerical and categorical features (often after appropriate encoding for categorical data).

Second, the predicted target in classification is indeed a categorical label, but that does not imply that inputs must be exclusively categorical. The output being categorical is the defining trait of classification, while the input feature types are not restricted to one kind.

Third, some special cases exist where inputs are purely numerical (for example, classifying iris species using sepal/petal measurements). This further demonstrates that numerical inputs are perfectly valid in classification tasks.

Fourth, if we were limited to only categorical inputs, many practical problems would be less solvable or would require heavy encoding, but the broader field and common practice embrace mixed or numerical inputs as well.

In summary, the claim is inaccurate because classification problems do not require inputs to be exclusively categorical; they can use numerical, categorical, or a combination of features to predict a categorical output.

MS MS Machine Learning (Georgios A Lentzas) SP2025 Quiz 1

Classification problems involve using only categorical inputs to predict a categorical output.

查看解析

登录即可查看完整答案

类似问题

MC 8-8 (LO 8-2) Predicting which companies are likely to be fraudulent/not fraudulent or likely to be bankrupt or not bankrupt are examples of... Predicting which companies are likely to be fraudulent/not fraudulent or likely to be bankrupt or not bankrupt are examples of ________ blank .

A machine learning system categorizes agricultural crops based on climatic data, soil conditions, and historical yield patterns to identify which crops are most suitable for cultivation during different seasons. This system is primarily performing which type of AI task?

What is classification?

Which of the following forms of data mining assigns records to one of a predefined set of classes?

Which of the following statements about classification in machine learning are correct?

In the linear classification example, what is the significance of defining curves in the manifold?

Predicting which companies are likely to be go bankrupt would be an example of which of the following?

What tasks are suitable for the classification techniques?(more than one answer)

更多留学生实用工具

考试浏览器助手

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