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 p
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