What is Decision Tree Classification in machine learning?
A fundamental ML concept question testing knowledge of supervised learning algorithms and their decision-making logic.
Why Interviewers Ask This
Decision trees are foundational models. Understanding them indicates readiness to tackle more complex ensemble methods and interpretability tasks.
How to Answer This Question
Define it as a flowchart-like structure. Explain splitting criteria (Gini, Entropy). Mention leaf nodes as predictions. Discuss pros (interpretability) and cons (overfitting).
Key Points to Cover
- Flowchart structure
- Splitting criteria
- Leaf node prediction
- Overfitting risk
Sample Answer
Decision Tree Classification is a supervised learning algorithm that splits data into subsets based on feature values to classify outcomes. It builds a tree where internal nodes represent tests on attributes, branches re…
Common Mistakes to Avoid
- Confusing with regression trees
- Missing splitting metrics
- Ignoring overfitting issues
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