Decision tree

Decision trees are a powerful tool in machine learning and data analysis. They are versatile and can be used for both classification and regression tasks. One of the key advantages of decision trees is their ability to handle both numerical and categorical data. Additionally, decision trees are easily interpretable, making them a popular choice for understanding the underlying logic behind a model’s predictions. As the tree grows, it creates a structure that represents a series of decisions based on feature values, ultimately leading to a prediction. This tree-like structure can be visualized and understood, providing valuable insights into the factors influencing the outcome.

Quizzes

Question: In a decision tree, what is a leaf node?
A. A node that splits into two or more sub-nodes.
B. A node that represents a decision point.
C. A node that does not split further and represents an outcome.
D. A node that has the maximum information gain.

Question: What is pruning in the context of decision trees?
A. Adding more nodes to increase the depth of the tree.
B. Removing nodes to reduce the complexity of the model and avoid overfitting.
C. Splitting nodes based on different criteria.
D. Combining nodes to increase the model’s accuracy.

Question: Which of the following methods can help prevent overfitting in decision trees?
A. Increasing the maximum depth of the tree.
B. Using more features for splitting nodes.
C. Pruning the tree.
D. Using a single decision tree without any ensemble methods.