The test set consists of a set of labelled examples similar to the training set, but which the model has not seen before during its training process. The machine learning model uses the test set to evaluate its ability to accurately generalise and predict output labels for new examples.
The test set consists of a set of labeled examples similar to the training set, but which the model has not seen before during its training process. The machine learning model uses the test set to evaluate its ability to accurately generalize and predict output labels for new examples.
The evaluation of the model on the test set helps determine whether the model is overfitting or underfitting the training data. Overfitting occurs when the model over-fits the training data and does not generalise well to new data, while under-fitting occurs when the model does not fit the training data well enough and cannot accurately predict the test data.
It is important to have an independent test set to evaluate the model's performance, as using the training set for evaluation can lead to an optimistic assessment of the model's accuracy.
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