Evaluation Metrics
Evaluation metrics are quantitative measures used to assess the performance of a machine learning model. These metrics are used to evaluate how well the model is performing in terms of accuracy, precision, recall, F1 score, and other relevant measures. The choice of evaluation metrics depends on the type of problem being solved and the goals of the model. For example, in a binary classification problem, accuracy, precision, recall, and F1 score are commonly used metrics. In a regression problem, mean squared error (MSE) and root mean squared error (RMSE) are commonly used metrics. Evaluation metrics are used to compare different models and to select the best model for a given problem.
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