Metrics
In Data Science, metrics refer to the quantitative measures used to evaluate the performance of a model or algorithm. Metrics are used to assess the accuracy, precision, recall, F1 score, AUC-ROC, and other performance indicators of a model. Metrics are essential in determining the effectiveness of a model and its ability to generalize to new data. Metrics are also used to compare different models and select the best one for a particular task. In addition, metrics are used to monitor the performance of a model over time and detect any degradation in its performance. Overall, metrics play a crucial role in the development and evaluation of machine learning models.
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