What is Decision Tree Classification and how does it work?
A machine learning question testing knowledge of supervised learning algorithms and their decision-making process.
Why Interviewers Ask This
Decision Trees are foundational algorithms in ML. Interviewers ask this to check if you understand the underlying mechanics, such as splitting criteria (Gini, Entropy), pruning, and handling overfitting.
How to Answer This Question
Define it as a supervised learning algorithm. Explain the hierarchical structure of nodes and leaves. Describe how splits are chosen based on information gain or Gini impurity. Mention the risk of overfitting and how pruning helps.
Key Points to Cover
- Supervised learning definition
- Tree structure explanation
- Splitting criteria
- Overfitting prevention
Sample Answer
Decision Tree Classification is a supervised learning algorithm that predicts categories by making a series of decisions based on input features. It builds a tree-like model where internal nodes represent feature tests a…
Common Mistakes to Avoid
- Confusing regression with classification
- Not explaining splitting metrics
- Ignoring overfitting risks
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