Which example below lists only classic model families?

Question

BlackTom AI · Accepted Answer

Decision trees, random forests, and support vector machines are all established, well-defined model families in machine learning.

1) Relevant knowledge points and concepts
- Model family: a class of algorithms that share a common mathematical formulation or structure (e.g., tree-based models, kernel methods, linear models).
- Paradigms vs. families vs. tasks: paradigms (supervised, unsupervised, reinforcement) describe learning settings; tasks (image classification) describe application problems; model families (decision trees, SVMs) are algorithmic approaches.

2) Step-by-step reasoning / classification of each option
- Option A: "Reinforcement learning, deep learning, rule-based systems"
  - Reinforcement learning = learning paradigm (framework for sequential decision problems).
  - Deep learning = a broad set of architectures (a subfield/paradigm based on neural networks), not a single classic "model family" in the traditional sense.
  - Rule-based systems = knowledge-engineering approach, not a standard statistical model family.
  => Mixed types, not all classic model families.
- Option B: "Clustering, supervised learning, linear models"
  - Clustering = unsupervised learning paradigm/task.
  - Supervised learning = learning paradigm.
  - Linear models = true model family (e.g., linear regression, logistic regression).
  => Mixed; only one is a model family.
- Option C: "Image classification, speech recognition, translation"
  - These are application tasks, not model families.
  => Incorrect.
- Option D: "Decision trees, random forests, support vector machines"
  - Decision trees = classic model family (tree-structured predictors).
  - Random forests = ensemble model family built from trees (also classical ensemble methods).
  - Support vector machines = classic kernel/margin-based model family.
  => All three are established model families.

3) Intermediate steps / checks
- Confirm each item in the chosen option maps to a formal algorithmic family with standard definitions, training methods, and theory (yes for D).

4) Why the correct answer is correct
- Each item in option D is an algorithmic family with canonical implementations and theoretical grounding; they are commonly taught as classic models in ML curricula.

5) Why other options are incorrect
- They mix paradigms, applications, and approaches rather than listing only model families.

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

Which example below lists only classic model families?Single choice

Similar Questions

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.

Which example below lists only classic model families?

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