RANSAC

RANSAC, which stands for Random Sample Consensus, is an algorithm used in data analysis to identify a model that best fits a set of data points, even when some of those points are outliers or noise. It works by repeatedly selecting a random subset of the data, fitting a model to this subset, and then checking how many data points fit this model well. Over many iterations, RANSAC finds the most reliable model based on the largest number of inliers, allowing for accurate predictions or interpretations despite the presence of misleading or erroneous data.