Is testing a ML model against data that could have been previously used in model training a proper evaluation phase in machine learning?
The evaluation phase in machine learning is a critical step that involves testing the model against data to assess its performance and effectiveness. When evaluating a model, it is generally recommended to use data that has not been seen by the model during the training phase. This helps to ensure unbiased and reliable evaluation results. However, it is also important to consider the potential impact of using data that was previously used in model training.
Using data that was previously used in model training can lead to overfitting, where the model performs exceptionally well on the training data but fails to generalize well to unseen data. This can happen because the model has essentially memorized the training data and is unable to make accurate predictions on new data. Including such data in the evaluation phase can give a false sense of the model's performance, as it may perform well on the known data but poorly on new, unseen data.
To avoid this issue, it is generally recommended to use a separate set of data for evaluation purposes, often referred to as a validation set or a holdout set. This data should be representative of the real-world data that the model is expected to encounter. By using this separate set of data, we can obtain a more accurate and unbiased assessment of the model's performance.
Furthermore, it is important to note that the evaluation phase is not limited to a single evaluation metric or technique. Different evaluation metrics and techniques can be used depending on the specific problem and requirements. For example, in classification problems, metrics such as accuracy, precision, recall, and F1 score can be used to evaluate the model's performance. In regression problems, metrics such as mean squared error (MSE) or mean absolute error (MAE) can be used.
While it is not recommended to use data that was previously used in model training for evaluation purposes, it is essential to use a separate set of data to obtain accurate and unbiased assessment results. This helps to ensure that the model's performance is evaluated in a realistic and reliable manner.
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