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:

• Mean squared error (MSE): measures the average of the squared errors between predictions and actual values.
• Root mean square error (RMSE): is the square root of the mean square error and is used to interpret the magnitude of the error in the same unit as the values of the target variable.
• Mean absolute error (MAE): measures the mean of the absolute errors between predictions and actual values.
• Median absolute error (MAD): measures the median of the absolute errors between predictions and actual values.
• Coefficient of determination (R^2): measures the proportion of the variance in the data that is explained by the model.
• Accuracy: measures the proportion of positive cases that were correctly classified.
• Recall: measures the proportion of actual positive cases that were correctly identified by the model.
• F1-score: is a measure of accuracy and recall, which combines both metrics into a single score.

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.

Link | Knowledge Generation based on Machine Learning and Application in Different Sectors