Practical Data Science using Python - Decision Tree - Gini Index and Entropy Measures

What does a Gini index of zero signify in a decision tree?

• Absolute purity

• Complete impurity

• Equal distribution

• Maximum entropy

How is the Gini index affected by a 50-50 distribution of classes?

• It becomes zero

• It becomes one

• It becomes 0.75

• It becomes 0.5

How is entropy different from the Gini index?

• Entropy measures purity

• Entropy is used for regression

• Entropy is always zero

• Entropy can exceed one in non-binary classification

What is the Iris dataset commonly used for in machine learning?

• Clustering

• Regression analysis

• Classification algorithms

• Time series analysis

Which feature was found to provide the lowest Gini index in the Iris dataset example?

• Petal length

• Sepal width

• Sepal length

• Petal width

What is the Gini index of a node with a pure class distribution?

• 0.667

• 0.5

• 0

• 1

What does a Gini index of 0.667 at the root node indicate?

• Complete impurity

• Absolute purity

• One-third and two-thirds split

• Equal distribution of classes

What is one stopping criterion for decision trees?

• Increasing the Gini index

• Maintaining the same Gini index

• Reducing the Gini index

• Achieving a higher entropy

Why is regularization important in decision trees?

• To increase the number of layers

• To achieve absolute purity

• To prevent overfitting

• To ensure maximum entropy

What is the role of hyperparameters in decision trees?

• To increase the Gini index

• To limit the number of layers

• To ensure maximum entropy

• To achieve absolute purity