I have a feature with all the values between 0 and 1 except few outliers larger than 1. I am trying to collect all the methods that can help to decrease outliers' influence on non-tree models:

  • StandardScaler
  • Apply rank transform to the features
  • Apply np.log1p(x) transform to the data
  • MinMaxScaler
  • Winsorization

I wasn't able to imagine any other ... I guess that's all?

  • 1
    $\begingroup$ There's also removing the instances with outlier values, if that's an option. $\endgroup$
    – Erwan
    Oct 15, 2020 at 0:01
  • $\begingroup$ RandomScaler does outlier treatment with IRQ. $\endgroup$ Dec 6, 2020 at 19:30

1 Answer 1


Here are a couple of other options:

  • Set a threshold and remove all values larger than the threshold.

  • Apply RobustScaler which removes the median and scales the data according to the quantile range.

  • Apply QuantileTransformer which transforms the feature to follow a uniform or a normal distribution.


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