以下关于决策树的陈述哪一个是正确的？选中所有正确的陈述。

Which of the following statements about decision trees are correct? Select all correct statements.

Question

以下关于决策树的陈述哪一个是正确的？选中所有正确的陈述。

Which of the following statements about decision trees are correct? Select all correct statements.

BlackTom AI · Accepted Answer

Decision trees are supervised learning models that learn a mapping from input attributes to target labels by training on a labeled dataset; they accept attributes of many types (categorical, Boolean, numeric) and produce predictions at leaves even when leaves are not perfectly pure.

1) Relevant knowledge points and concepts
- Supervised training: decision trees are induced from labeled datasets; induction chooses splits to partition data to improve class separation.
- Attribute types: features can be Boolean, categorical, ordinal, or continuous; algorithms handle these either natively (categorical/Boolean) or after discretization (continuous).
- Leaf purity: a pure leaf contains only one class, but trees commonly produce impure leaves and still predict using majority class or class probabilities.
- Dataset reuse and attribute order: the same dataset can be used to train multiple different trees (different algorithms, randomization, or parameter settings); attributes do not need to be pre-ordered for training.

2) Step-by-step reasoning applied to each option
- Option 1: “We need to train a decision tree using a dataset before using it to classify data.” A decision tree must be induced (trained) from labeled examples to be able to predict new instances (unless a tree is already prebuilt). Therefore this is correct.
- Option 2: “A dataset can be used for training a single decision tree only, where the attributes must be ordered in certain way.” This is false: the same dataset can train many different trees (different splits, random seeds, ensemble methods), and attributes do not require any special ordering—the algorithm selects split attributes.
- Option 3: “A decision tree can make a decision only if each leaf node corresponds to a pure subset.” This is false: leaves can be impure; predictions are typically the majority class or class probability at the leaf.
- Option 4: “The attributes ... can contain Boolean ... as well as other types of values.” This is true: attributes may be Boolean, categorical, numerical, etc.

3) Calculations
- No numeric calculation is required.

4) Why correct answers are correct
- Training is required to build the tree model; attribute types are flexible and include Boolean.

5) Why incorrect options fail
- Option 2 wrongly restricts dataset use and imposes attribute ordering; Option 3 incorrectly states purity as a requirement.

Conclusion: The correct statements are the first and the fourth options, matching the provided answer.

以下关于决策树的陈述哪一个是正确的？选中所有正确的陈述。 Which of the following statements about decision trees are correct? Select all correct statements. Multiple choice

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