title: How does the Multivariate imputer in scikit-learn differ from the Simple imputer?
body: <p>I have a matrix of data with missing values that I am trying to impute, and I am looking at the options for different imputers and checking to see what settings would work best for the biological context I am working in. I understand the knnimpute function in matlab and the simple imputer in scikit-learn. However, I'm not quite sure my understanding of the iterative imputer is correct. </p>
  
  <p>I have looked at the documentation at this site for the multivariate/iterative imputer -- <a href="https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html" rel="nofollow noreferrer">https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html</a></p>
  
  <p>I don't understand the explanation of the algorithm, as round-robin. Does the imputer use the characteristics of both the column and rows in the matrix to determine the "value" of a missing data point? Then taking that approach one random missing data point at a time to avoid shifting the data unnaturally towards the characteristics of a previously imputed data point?</p>