# For outliers treatment: clipping, winsorizing or removing?

I came across three different techniques for treating outliers winsorization, clipping and removing:

• Winsorizing: Consider the data set consisting of: {92, 19, 101, 58, 1053, 91, 26, 78, 10, 13, −40, 101, 86, 85, 15, 89, 89, 28, −5, 41} (N = 20, mean = 101.5) The data below the 5th percentile lies between −40 and −5, while the data above the 95th percentile lies between 101 and 1053. (Values shown in bold.) Then a 90% winsorization would result in the following: {92, 19, 101, 58, 101, 91, 26, 78, 10, 13, −5, 101, 86, 85, 15, 89, 89, 28, −5, 41} (N = 20, mean = 55.65)

• Clipping:Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.

• Removing: Just taking them out.

My questions are:

1. In which cases should I use which one?
2. If I always do winsorization (which seems the best in my opinion) when I am losing important information?
3. Is this model-dependent (for decision trees, for linear...) or the same strategy can be applied to all of them