Question at position 15 Which is not a technique for reducing/avoiding over-fitting in tree induction?Choose largest improvement in information gainReduce tree size based on statistical testSelect tree size based on validation dataPrune tree

Question

BlackTom AI · Accepted Answer

Overfitting in decision trees arises when the model captures noise or idiosyncrasies of the training data, producing excessive tree complexity and poor generalization. Key concepts: bias–variance tradeoff, pre-pruning vs post-pruning, statistical stopping criteria, and validation-based model selection.

1) Relevant knowledge points and concepts
- Information gain (or gain ratio) is a greedy split criterion used during tree growth to pick the best attribute at each node.
- Pre-pruning techniques stop growth early based on conditions (e.g., statistical significance tests).
- Post-pruning (prune tree) removes branches after full growth to reduce complexity.
- Validation-based selection chooses tree size that minimizes error on held-out data.
- Methods that explicitly limit tree complexity or validate generalization help reduce overfitting.

2) Step-by-step reasoning
- Evaluate each option as a technique to reduce/avoid overfitting.
- "Choose largest improvement in information gain": this is a local, greedy decision rule for selecting splits. It focuses only on immediate purity gain, not on overall tree complexity or statistical reliability of the split. As a result it can favor splits that fit noise (high variance) and thus does not inherently reduce overfitting; it is not a complexity-control technique.
- "Reduce tree size based on statistical test": this describes pre-pruning using statistical significance (e.g., chi-square) to decide whether to split—an explicit anti-overfitting method.
- "Select tree size based on validation data": using a validation set to pick tree complexity directly targets generalization performance—standard method to avoid overfitting.
- "Prune tree": post-pruning (e.g., cost-complexity pruning) reduces overfitting by removing branches that do not improve validation performance.

3) Calculations
- No numerical calculations are required; the reasoning is conceptual and comparative.

4) Why the correct answer is correct
- Choosing the split with largest information gain is a greedy growth heuristic, not a regularization or validation/pruning method. It can increase overfitting rather than reduce it.

5) Why other options are incorrect (briefly)
- Reduce tree size based on statistical test: is pre-pruning — reduces overfitting.
- Select tree size based on validation data: is model selection — reduces overfitting.
- Prune tree: is post-pruning — reduces overfitting.

Conclusion: The answer "Choose largest improvement in information gain" is not a technique for reducing/avoiding overfitting, which matches the provided correct answer.

Question at position 15 Which is not a technique for reducing/avoiding over-fitting in tree induction?Choose largest improvement in information gainReduce tree size based on statistical testSelect tree size based on validation dataPrune tree单项选择题

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