Which example below lists only classic model families?

Question

BlackTom AI · Accepted Answer

Decision trees, random forests, support vector machines are all algorithmic model families commonly taught as classic supervised learning methods.

Relevant knowledge points:
- Model family vs. task vs. paradigm: A model family is a set of algorithms that share the same underlying structure (e.g., trees, kernel methods). A task is an application/problem (e.g., image classification). A paradigm refers to a broader approach or setting (e.g., reinforcement learning, supervised learning).
- Classic model families include tree-based models (decision trees and their ensembles like random forests), linear models, and kernel-based methods (support vector machines).

Step-by-step reasoning:
1. Examine Option A: "Decision trees, random forests, support vector machines."
   - Decision trees: a specific model family using tree-structured rules.
   - Random forests: an ensemble model family built from multiple decision trees (still a model family).
   - Support vector machines (SVMs): a kernel-based model family for classification/regression.
   All three are algorithmic model families — consistent and classic. Therefore Option A is correct.

2. Examine Option B: "Image classification, speech recognition, translation."
   - These are tasks/applications (problem domains), not model families. Different models can solve each task.

3. Examine Option C: "Clustering, supervised learning, linear models."
   - Clustering: an unsupervised learning task/paradigm.
   - Supervised learning: a learning paradigm.
   - Linear models: a model family.
   This option mixes paradigms and a model family, so it does not list only model families.

4. Examine Option D: "Reinforcement learning, deep learning, rule-based systems."
   - Reinforcement learning: a paradigm.
   - Deep learning: an approach/modeling paradigm (a broad family of architectures but often considered a methodological paradigm rather than a single classic model family in the same sense).
   - Rule-based systems: knowledge-engineering systems, not statistical model families.
   This mix disqualifies Option D.

Why the correct answer is correct:
Option A contains three well-defined, classic model families used for supervised learning, each representing a distinct algorithmic family (tree-based, ensemble, and kernel-based).

Conclusion: The correct choice is "Decision trees, random forests, support vector machines," matching the provided answer.

Which example below lists only classic model families?单项选择题

类似问题

Which example below lists only classic model families?

Learning types describe how a model learns based on the available data and learning signal, while model families describe what kind of algorithm or model is used.

Deep learning is a learning type, just like supervised learning and unsupervised learning.

Supervised learning uses target values or labels during training, while unsupervised learning looks for patterns without given target values.

Question at position 26 Large Language Models typically require significantly less computational power than a Small Language Model as they are built in a more streamlined manner, and the models are typically more efficient.TrueFalse

Which of the following statements about machine learning is not true?

The concept of machine learning typically involves (check all that apply)

The concept of machine learning typically involves (check all that apply)

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