> ## Documentation Index
> Fetch the complete documentation index at: https://shapely.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Mean Absolute Error

>  Mean Absolute Error (MAE) measures the average magnitude of errors between predicted and actual values. It calculates the mean of the absolute differences, giving you a clear picture of how far off predictions are on average.


<Note>
  * **MAE is expressed in the same units** as your target variable, making it easy to interpret. For example, if predicting area in m², an MAE of 5.0 means predictions are off by 5 m² on average. Use MAE to compare different regression models and choose the most accurate one.

  * **Lower MAE values indicate better prediction accuracy**
</Note>

### Use Cases

* **Model evaluation**: Measure how accurately your regression model predicts continuous values.
* **Model comparison**: Compare MAE scores across different algorithms (Linear, Random Forest, Gradient Boosting) to select the best one.
* **Performance monitoring**: Track prediction accuracy as you add more features or data to your training set.