Machine Learning Algorithms - Second Edition

RANSAC

A common problem with linear regressions is caused by the presence of outliers. An ordinary least- square approach will take them into account and the result (in terms of coefficients) will be therefore biased. In the following graph there's an example of such a behavior:

Example of a dataset containing outliers

The shallower-sloped line represents an acceptable regression that discards the outliers, while the other one is influenced by them. An interesting approach to avoid this problem is offered by RANSAC, which works with every regressor by subsequent iterations, after splitting the dataset into inliers and outliers. The model