The term "greedy algorithms" refers to machine-learning algorithms that:

→ Greedy algorithms make decisions based on what appears to be the best (most optimal) choice at that current moment — i.e., a locally optimal decision — without regard to whether this choice will yield the globally optimal solution.

Examples in machine learning:

Decision Tree algorithms (e.g., CART) use greedy approaches by selecting the best split at each node based on information gain or Gini index.

Why the other options are incorrect:

A: This refers to Bayesian updating, not greedy behavior.

B: That describes exhaustive search, not greediness.

C: That aligns more with probabilistic or generative models, not greedy strategies.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.2 (Model Selection Methods):“Greedy algorithms make locally optimal decisions at each step. Decision trees, for instance, use greedy splitting based on current best criteria.”

Elements of Statistical Learning, Chapter 9:“Greedy methods make stepwise decisions that maximize immediate gains — they are fast, but may miss the global optimum.”

—