In the context of modeling in artificial intelligence and machine learning, error metrics are measures used to assess the quality of predictive and classification models. These metrics allow quantifying the difference between model predictions and actual values, which allows comparing the performance of different models and selecting the best model for a specific task.
Some of the most common error metrics used in modeling are as follows:
These error metrics are valuable tools for evaluating the performance of artificial intelligence and machine learning models and adjusting their parameters to improve their accuracy and generalization.
The linked documents describe some of the most important error calculations used in the problems of prediction y classification.
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