Which can be applied over time to refine a model to better predict results?

• A. Association rule learning
• B. Statistics
• C. Regression
• D. Machine learning

Answer: (D)

The idea being tested is the ability to improve predictions by learning from data over time. This is what machine learning is all about: using data to train models, then updating and refining those models as new data comes in, so their predictions get better with experience. It’s common to retrain models, incorporate new information, and even use online or incremental learning to adapt to changing patterns, which is central to providing more accurate results as conditions evolve.

Association rule learning focuses on finding interesting relationships or patterns in data, rather than continuously improving predictive accuracy of a model. Statistics provides foundational tools for inference and modeling, but the ongoing, data-driven refinement process described is most characteristic of machine learning. Regression is a specific predictive method used within statistics or machine learning, but on its own it doesn’t capture the full idea of evolving, iterative improvement through data over time in the broader sense.