Description: An evaluation set is a subset of data used to measure the performance of a machine learning model after it has been trained. This set is crucial for validating the model’s ability to generalize to unseen data, meaning its effectiveness is assessed in real-world situations. Typically, the evaluation set is separated from the training set, which is used to adjust the model’s parameters. The idea is that by evaluating the model on a different dataset, a more accurate estimate of its performance can be obtained. A good evaluation set should be representative of the problem being solved and contain examples that the model has not seen during training. This helps to avoid overfitting, where the model adapts too closely to the training data and loses its generalization capability. In the context of hyperparameter optimization and model selection, the evaluation set is used to compare different model configurations and select the one that offers the best performance. In summary, the evaluation set is an essential tool in machine learning, as it provides an objective measure of a model’s effectiveness.
