Test: Difference between revisions

In machine learning, testing is like a final exam for a model that has been studying some data. We give the model a separate set of questions, called a test set, to see how well it learned from the data it studied. This helps us find out if our model is good at solving real-world problems or if it just memorized the study material. We also use something called evaluation metrics to measure how well our model did on the test. These metrics help us understand how good our model is at solving the specific problem we want it to solve.

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