Huber Regressor

Huber Regressor is a robust regression method that combines squared loss for small errors and absolute loss for large errors. This makes it significantly less sensitive to outliers than standard linear regression.

It provides the accuracy of linear regression on clean data while automatically reducing the impact of anomalous points.

If your dataset contains outliers that you cannot or do not want to remove, Huber Regressor is an excellent choice.

• Noisy geometric data: Predict properties when some measurements in the dataset are unreliable or erroneous.
• Mixed-quality data: When data from different sources has varying quality levels and some entries may contain extreme values.
• Robust predictions: Any regression scenario where you suspect outliers could distort the model.

Use Cases

• Noisy geometric data
• Mixed-quality data
• Robust predictions