Which of the following scenarios is most likely to cause a Decision Tree to overfit?

Question

BlackTom AI · Accepted Answer

Allowing the tree to grow until each leaf node is pure (has one sample) creates a model that perfectly fits the training data but likely captures noise, so it is the most likely cause of overfitting.

1. Relevant knowledge points and concepts
- Overfitting: model fits training data noise and idiosyncrasies, performing poorly on new data (high variance, low bias).
- Decision tree complexity: depth and number of leaves control model capacity. More splits increase flexibility and variance.
- Pure leaf (one sample): maximal partitioning of training data; each leaf perfectly classifies its single training instance.
- Regularization techniques: pruning, limiting depth, and setting minimum samples per leaf reduce overfitting.

2. Step-by-step reasoning
- If a tree is allowed to split until each leaf contains exactly one sample, the tree will represent every training instance uniquely.
- Step 1: For each training sample, the tree creates a distinct path of splits to isolate it.
- Step 2: The splits may be based on noise or irrelevant feature values because the criterion (e.g., Gini or entropy) favors pure leaves.
- Step 3: On unseen data, such finely partitioned decision boundaries will not generalize; small variations in input lead to misclassification.

3. Intermediate steps (logical)
- Training error → 0 because each sample is correctly classified.
- Model complexity → maximal (very deep, many leaves).
- Generalization error → increases because variance dominates; model sensitive to training noise.

4. Why the correct answer is correct
- Pure leaves imply maximum specialization to training examples. That specialization is the classic mechanism behind overfitting: the model memorizes rather than learns general patterns.

5. Why other options are incorrect
- Pruning too many branches: reduces complexity and tends to cause underfitting, not overfitting.
- Using too few features for splits: restricts model capacity and typically increases bias (underfitting), not overfitting.
- Applying a high minimum support threshold: (interpreting analogous constraints like high min samples per leaf) prevents small leaves and reduces overfitting; it forces broader generalization.

6. Conclusion
Allowing the tree to grow until each leaf node is pure (has one sample) is the most likely scenario to cause overfitting, which matches the provided correct answer.

Which of the following scenarios is most likely to cause a Decision Tree to overfit?单项选择题

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