A . TRUE. The common decision tree algorithms include ID3, C4.5, and CART. These are the most widely used algorithms for decision tree generation.
B . FALSE. Purity in decision trees can be measured using multiple metrics, such as information gain, Gini index, and others, not just information entropy.
C . TRUE. Building a decision tree involves selecting the best features and determining their order in the tree structure to split the data effectively.
D . TRUE. One key step in decision tree generation is evaluating the purity of different splits (e.g., how well the split segregates the target variable) by comparing metrics like information gain or Gini index.
HCIA AI
Reference:
Machine Learning Overview: Covers decision tree algorithms and their use cases.
Deep Learning Overview: While this focuses on neural networks, it touches on how decision-making algorithms are used in structured data models.