Mean absolute error(MAE) is a popular performance metric used in machine learning to assess the accuracy of a model. It is a simple technique that measures the difference between the predicted values and actual values of a dataset. It involves calculating the average of the absolute differences between the predicted values and the actual values.

In simple words, MAE is a way of measuring how well a model can predict the outcomes of a given dataset. The lower the MAE score, the better the model’s performance. MAE is an extremely useful metric that can be applied to a range of problems, including regression, classification, and clustering.

The mathematical formula for calculating the mean absolute error is:

MAE = (1/n) ∑i=1 to n| Yi – Ŷi |

Where Yi is the actual value for observation i, and Ŷi is the predicted value for observation i. n represents the number of observations in the dataset.

Simply put, MAE is the average of the absolute differences between the predicted and actual values. It is a more intuitive metric than other performance measures like Mean Squared Error (MSE).

MAE is particularly useful when dealing with continuous variables. For example, it is commonly used to evaluate the accuracy of models predicting house prices, stock prices, or weather forecasts. In these scenarios, it is crucial to find a model that can predict the target value as accurately as possible.

MAE is often used in conjunction with other metrics, such as Root Mean Squared Error (RMSE) and R-squared, to provide a more comprehensive evaluation of a model’s performance. RMSE takes the square root of the average of the squared differences between the predicted and actual values. R-squared measures how well the model fits the underlying data.

To summarize, Mean Absolute Error (MAE) is a popular metric used to evaluate the accuracy of machine learning models. It measures the absolute differences between the predicted and actual values of a dataset. The lower the MAE score, the better the performance of the model. It is particularly useful for dealing with continuous variables and can be used in combination with other metrics to provide a more robust evaluation of a model’s performance.