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Test in ML is used to evaluate the performance of the final model.

When you're testing a predictive model, you're assessing how well it performs on a separate dataset that it has not seen during the training process. This dataset is often referred to as the "test set" or "holdout set." The purpose of this testing phase is to provide an independent assessment of the model's performance and its ability to make accurate predictions on new, unseen data. Various evaluation metrics are used during this phase to determine how well the model generalizes and whether it meets the desired performance criteria.

Testing is a critical step in the predictive modeling process, as it helps ensure that the model is robust and can be trusted to make predictions in real-world scenarios. It allows you to estimate how well the model will perform when deployed for practical use.

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