In supervised learning, what is true about the dataset?

Question

BlackTom AI · Accepted Answer

Supervised learning requires a dataset made of input–output pairs: each training example is an input together with its correct answer (label), which the algorithm uses to learn the mapping from inputs to targets.

1. Relevant knowledge points and concepts
- Supervised learning: training a model to predict outputs y from inputs x using labeled examples.
- Dataset form: D = {(x1, y1), (x2, y2), ..., (xn, yn)} where xi are features and yi are labels (class, continuous value, etc.).
- Learning objective: find parameters θ of a function fθ(x) that minimize a loss L(fθ(xi), yi) across the dataset so the model generalizes to new inputs.

2. Step-by-step reasoning
- By definition, supervised learning uses labeled data. Each example provides a target that tells the model what the correct prediction should be.
- The model evaluates predictions against those labels via a loss and updates parameters (e.g., gradient descent) to reduce error.
- Because labels are provided, the model can compute an explicit supervision signal for every example during training.

3. Intermediate (symbolic) steps
- Represent dataset D = {(x1, y1), ..., (xn, yn)}.
- Choose model fθ and loss L; compute empirical risk R(θ) = (1/n) Σi L(fθ(xi), yi).
- Optimize θ* = argminθ R(θ). The yi values are required in R(θ), so labels must exist for each xi.

4. Why the correct answer is correct
- The correct answer states “Each example has a correct answer that the system can learn from,” which exactly matches the definition and mathematical formulation of supervised learning: every training example includes a label used to compute loss and guide learning.

5. Why other options are incorrect
- “It contains only inputs with no correct answers”: describes unsupervised learning, not supervised.
- “The system creates its own labels automatically”: describes self-supervised or some automated labeling approaches, not standard supervised learning.
- “It contains a mix of labeled and unlabeled examples”: describes semi-supervised learning, not pure supervised learning.

6. Conclusion
The correct choice is “Each example has a correct answer that the system can learn from,” which matches the provided answer.

In supervised learning, what is true about the dataset?单项选择题

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