A dairy cooperative is building a milk-spoilage predictor. They hold eight years of labeled cooling-log examples, they chose gradient boosting as the learning procedure, and after the job finishes they will ship a file that scores new cooling logs. Which object is the learned artifact they will deploy?
Select an answer to reveal the explanation.
Short Explanation
Think of it like baking. The recipe is the algorithm, the labeled cooling logs are the ingredients, and the finished cake is the model you actually serve. Sensors just measure the kitchen; they are not what you ship to score a new vat.
Full Explanation
A model is the learned artifact produced after a learning procedure has been fit to examples, and that is what the cooperative will deploy to score new cooling logs. The algorithm is the procedure (here, gradient boosting), not the fitted result. The dataset is the labeled history used during fitting, not the object that scores new vats. Sensors collect measurements; they are not a trained predictor.