Which of the following is an evaluation indicator of regression algorithms?

Mean squared error is a commonly used evaluation indicator for regression algorithms because it quantifies the difference between the predicted values generated by the model and the actual values from the dataset. Specifically, it calculates the average of the squares of the errors, which provides a sense of how far off the predictions are from the actual outcomes. The squaring of the errors ensures that larger mistakes are penalized more than smaller ones, making it a sensitive measure to outliers in the data.

In contrast, metrics like accuracy, recall, and confusion matrix are primarily associated with classification tasks rather than regression. Accuracy measures the proportion of correctly predicted instances in a classification context, while recall focuses on the ability of a model to capture positive cases. The confusion matrix presents a detailed breakdown of the true positives, true negatives, false positives, and false negatives for a classification model. These indicators do not apply to regression problems, where the goal is to predict continuous values rather than discrete classifications. Thus, mean squared error is the most appropriate choice for evaluating regression algorithms.