1. What is a Decision Tree?
A decision tree is a popular supervised machine learning algorithm used for classification and regression tasks. It is a flowchart-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome or prediction.

2. What are the key components of a Decision Tree?
The key components of a decision tree include the root node, internal nodes, branches, leaf nodes, feature/attribute, decision rules, and prediction/outcome.

3. What are the advantages of using Decision Trees?
Advantages of decision trees include interpretability, handling nonlinear relationships, feature importance measurement, robustness to outliers and missing values, and versatility in classification and regression tasks.

4. What are the limitations of Decision Trees?
Limitations of decision trees include overfitting, lack of smoothness in decision boundaries, instability with small changes in the data, and challenges in handling categorical variables.

5. What are some applications of Decision Trees?
Decision trees are commonly used for classification tasks, regression tasks, feature selection, anomaly detection, and decision support systems.

6. What are the steps involved in building a Decision Tree?
The steps involved in building a decision tree include data preprocessing, tree construction, recursive partitioning, pruning (optional), and evaluation.

7. What are some popular algorithms for Decision Tree construction?
Popular algorithms for decision tree construction include ID3, C4.5, and CART, which use different splitting criteria and handle categorical and continuous target variables.

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