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I have a short question. I am implementing Scikit-Learn in Typescript and currently blocked at understanding & implementing imputer (mean and regression strategies).

Based on the example given on Scikit-Learn page, http://scikit-learn.org/stable/modules/preprocessing.html#imputation

>>> import numpy as np
>>> from sklearn.preprocessing import Imputer
>>> imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
>>> imp.fit([[1, 2], [np.nan, 3], [7, 6]])
Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)
>>> X = [[np.nan, 2], [6, np.nan], [7, 6]]
>>> print(imp.transform(X))                           
[[ 4.          2.        ]
 [ 6.          3.666...]
 [ 7.          6.        ]]

How does imputation against np.nan in [[np.nan, 2], [6, np.nan], [7, 6]] work?

  • Could you please explain it to me how it works? I would appreciate any equations and background knowledge that I need to understand this.
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  • $\begingroup$ If you are asking about Scikit then your question is off topic here. If you are asking something more general about imputation, then please edit your question to reflect that. $\endgroup$
    – Peter Flom
    May 3, 2018 at 14:26
  • $\begingroup$ Is it fine to use Scikit Learn code as a reference? $\endgroup$
    – Jason Marsh
    May 4, 2018 at 10:10
  • $\begingroup$ -reference- -> example* $\endgroup$
    – Jason Marsh
    May 4, 2018 at 10:28
  • $\begingroup$ You can use code as a reference, but if you are trying to debug code, this is the wrong forum. $\endgroup$
    – Peter Flom
    May 4, 2018 at 10:45
  • $\begingroup$ Question: Don't you think it still belongs to this place because it's not really asking about implementation detail but asking about how it works? $\endgroup$
    – Jason Marsh
    May 7, 2018 at 5:18

1 Answer 1

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The Imputer is just calculating the mean for each column when fit is called. So column 1 has mean (1+7)/2 = 4 and column 2 has mean (2+3+6)/3 = 3.666....

The transform function just fills in the NaN fields with the column's mean value.

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  • $\begingroup$ How about regression strategy? Could you explain that to me as well? Thanks in advance again $\endgroup$
    – Jason Marsh
    May 2, 2018 at 23:58
  • $\begingroup$ There is no regression strategy in sklearn, but you could do this yourself by training a model using the other columns as the dataset and the column to be imputed as the target, nearest neighbor is a simple and fast way to do this (use the value of your closest neighbor(s) for each missing field). $\endgroup$
    – anthonybell
    May 3, 2018 at 0:10

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