What is Decision Tree Classification and how does it work?
This question asks for an explanation of a fundamental supervised learning algorithm used for classification tasks.
Why Interviewers Ask This
Decision trees are a baseline model for many ML problems. Interviewers check if you understand the core logic of splitting data based on feature values and calculating impurity metrics.
How to Answer This Question
Explain the hierarchical structure of the tree. Describe how splits are chosen using criteria like Gini impurity or Information Gain. Mention pruning to prevent overfitting.
Key Points to Cover
- Hierarchical structure
- Split criteria (Gini/Entropy)
- Leaf nodes meaning
- Pruning technique
Sample Answer
Decision Tree Classification is a supervised algorithm that splits data into subsets based on feature values. It builds a tree-like model where internal nodes represent decisions and leaf nodes represent outcomes. Splits are chosen to maximize information gain or minimize Gini impurity. Pruning is often applied to reduce complexity and avoid overfitting.
Common Mistakes to Avoid
- Confusing regression with classification
- Not mentioning overfitting
- Vague explanation of split logic
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