Define model testing.

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Key Concepts

Test Dataset

A test dataset is a subset of the data that is reserved exclusively for the evaluation of a model after it has been trained. Its purpose is to provide an unbiased assessment of the model’s predictive accuracy and to ensure that the model generalizes well beyond the data it was trained on.

Generalization

Generalization refers to a model's ability to perform effectively on new, unseen data, not just on the data used during training. This concept is fundamental in model testing because it determines whether a model can be applied reliably to real-world scenarios beyond its training environment.

Model Testing

Model testing is the process of evaluating a predictive or analytical model using a separate dataset to assess its performance and generalization capabilities. It is a crucial step in the modeling process that helps determine how well the model can make accurate predictions on new, unseen data.

Evaluation Metrics

Evaluation metrics are quantitative measures used to assess the performance of a model during testing. These metrics, such as accuracy, precision, recall, or mean squared error, provide insights into various aspects of model performance and help compare different models or adjustments to the same model.

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