Average Error
Average error is a statistical measure used to quantify the discrepancy between predicted values and actual values across a dataset. In machine learning and data analytics, average error provides insights into the overall accuracy of a model by aggregating individual prediction errors and calculating their mean. It is particularly useful in regression tasks, where minimizing error is a primary goal. Average error can be expressed in various forms, such as mean absolute error (MAE) or mean squared error (MSE), each offering different perspectives on model performance.
https://en.wikipedia.org/wiki/Mean_absolute_error
The computation of average error involves summing the absolute or squared differences between predicted and observed values and dividing by the total number of observations. This metric helps developers and data scientists understand how well a model generalizes to unseen data. While mean squared error penalizes larger errors more heavily due to squaring, mean absolute error provides a more interpretable measure of typical deviation. Both metrics are commonly used during model evaluation and hyperparameter tuning in frameworks like TensorFlow and PyTorch.
https://en.wikipedia.org/wiki/Mean_squared_error
While average error is a simple yet effective metric, it does not account for the direction of errors (e.g., underestimation vs. overestimation). In cases where directionality matters, metrics like bias or signed error may complement average error calculations. Moreover, in classification tasks, other metrics such as accuracy, precision, and recall are preferred over average error. Nonetheless, average error remains a cornerstone in evaluating regression models and forms the basis for advanced metrics and techniques in predictive analytics.
https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html
https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanAbsoluteError