Huber Regressor

Huber Regressor is a robust regression algorithm that is less sensitive to outliers compared to traditional linear regression. It uses different loss functions, which combine the properties of Mean Squared Error (MSE) and Mean Absolute Error (MAE). For small Deviations, it acts like MSE whereas, for larger deviations, it acts as MAE. The Huber loss function provides more efficient results than traditional linear regression in the presence of outliers.

The algorithm can be used when the data has outliers and traditional linear regression would produce biased results. It is also useful when the outliers in the dataset have a larger impact on the result than the non-outliers.

Regression is a commonly used machine learning technique for predicting continuous outputs. In some datasets, outliers can have a significant impact on the results. To handle such datasets with outliers, two common algorithms are Huber Regressor and Ridge Regression. This article will explore the differences between the two algorithms in the context of outliers and when to use one over the other with examples.