Pre-processing - Removing outliers I have two files, a training data with a label field and a test data without the label field. I have plotted a field "A" in train data:



It looks like outliers are 4,5,6 and should be removed.

Now plotting the field "A" in test file also shows somewhat similar results to the image shown. In that case, should I consider 4,5,6 as outliers, and remove them from train data, or keep them? Any pointers are appreciated.
In general, its not acceptable to drop an observation just because it is an outlier.

Sometimes outliers give us very important information about data. 
Removing outliers is legitimate if:


it is obvious that's incorrect data
not change the results and assumptions.


How to handle outliers? Try other models or use data transformation.