Machine Learning Evaluation Metrics
Machine Learning Evaluation Metrics are quantitative measures used to assess the performance of a machine learning model. These metrics are used to evaluate the accuracy, precision, recall, F1 score, and other performance indicators of a model. The choice of evaluation metric depends on the type of problem being solved and the nature of the data. 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), mean absolute error (MAE), and R-squared are commonly used metrics. In addition to these metrics, there are other evaluation metrics such as confusion matrix, ROC curve, and AUC that are used to evaluate the performance of a model.
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