Decision trees are a widely usedmachine learning (ML) techniquethat falls undersupervised learning. They are used for bothclassification and regressiontasks and are popular due to their interpretability and effectiveness.
How Decision Trees Work:
The model splits the dataset into branches based on feature conditions.
It continues to divide the data until each subset belongs to a single category (classification) or predicts a continuous value (regression).
The final result is a tree structure where decisions are made atnodes, and predictions are given atleaf nodes.
Common Applications of Decision Trees:
Fraud detection
Medical diagnosis
Customer segmentation
Recommendation systems
B (Equivalence Partitioning):This is asoftware testing technique, not a machine learning method. It is used to divide input data into partitions to reduce test cases while maintaining coverage.
C (Boundary Value Analysis):Anothersoftware testing technique, used to check edge cases around input boundaries.
D (Decision Tables):A structuredtesting techniqueused to validate business rules and logic, not a machine learning method.
ISTQB CT-AI Syllabus (Section 3.1: Forms of Machine Learning - Decision Trees)
"Decision trees are used in classification and regression models and are fundamental ML algorithms".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sincedecision trees are a core technique in machine learning, while the other options aresoftware testing techniques, thecorrect answer is A.
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