An algorithm is the procedure or set of rules (for example, linear regression, k-means, or gradient boosting) that defines how a system should learn patterns from data. After the algorithm is applied during the training step, the output is a model that contains the learned parameters (such as weights or cluster centers). The model can then be used to generate predictions on new, unseen data. The other statements incorrectly swap these roles, confuse runtime inference with training, or equate algorithms with data or metrics.
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Fundamentals of AI and ML
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