An ensemble learning approach to finding these things relies on running the LOF algorithm repeatedly and is a form of "feature bagging". The RANSAC method can be used in the presence of large numbers of these things. Grubbs method for finding them relies on a test statistic consisting of the max residual over the standard deviation. Winsorization is one method of dealing with these values, which are also the motivation behind the development of so-called (*) "robust" methods. The existence of a large number of these entities assuming a normal prior may indicate a high kurtosis in the underlying distribution. These things are generally plotted above or below the "whis
An ensemble learning approach to finding these things relies on running the LOF algorithm repeatedly and is a form of "feature bagging". The RANSAC method can be used in the presence of large numbers of these things. Grubbs method for finding them relies on a test statistic consisting of the max residual over the standard deviation. Winsorization is one method of dealing with these values, which are also the motivation behind the development of so-called (*) "robust" methods. The existence of a large number of these entities assuming a normal prior may indicate a high kurtosis in the underlying distribution. These things are generally plotted above or below the "whiskers" in a box-and-whisker plot and are usually one point five times the interquartile range beyond the upper or lower quartile. For 10 points, name these points in a data set which do not fit within the presumed distribution.
An ensemble learning approach to finding these things relies on running the LOF algorithm repeatedly and is a form of "feature bagging".
outliers