Evaluation Metric

Evaluation metric is a quantitative measure used to assess the performance of a machine learning model. It is used to evaluate how well the model is performing on the given dataset. The choice of evaluation metric depends on the type of problem being solved and the goals of the model. For example, accuracy is a commonly used evaluation metric for classification problems, while mean squared error is used for regression problems. Other commonly used evaluation metrics include precision, recall, F1 score, area under the curve (AUC), and mean absolute error (MAE). It is important to choose an appropriate evaluation metric that aligns with the problem being solved and the desired outcome. A good evaluation metric should be easy to interpret, sensitive to changes in the model, and relevant to the problem at hand.
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