Body: <p><strong>BACKGROUND:</strong>  I am developing a deep learning model in which one feature variable (out of many) is the grade of cancer, an <a href="https://en.wikipedia.org/wiki/Ordinal_data" rel="nofollow noreferrer">ordinal variable</a>.  Below is a breakdown of my data by grade:</p>
  <pre><code>GRADE       SAMPLES
  --------------------
  Grade I     746
  Grade II    1128
  Grade III   2908
  Grade IV    1909
  Unknown     4255
  </code></pre>
  <p><strong>PROBLEM:</strong>  I need a solution to handle a large amount of missing data.</p>
  <p><strong>OPTIONS I HAVE CONSIDERED:</strong></p>
  <ol>
  <li>Because of the amount of missing data, just removing those study subjects is not an option (can't remove rows -- too many).</li>
  <li>Because grade is an important predictor variable, removing grade as a feature is also not an option (can't remove the column -- too important).</li>
  <li>I fear that imputation is not going to give me accurate results since so much of my data is missing.  I have considered <a href="https://scikit-learn.org/stable/modules/impute.html#imputation-of-missing-values" rel="nofollow noreferrer">multivariate imputation methods</a> as well, but I'm still not convinced I can get meaningful results.  Maybe I'm wrong.</li>
  <li>I can just treat grade like a <a href="https://en.wikipedia.org/wiki/Level_of_measurement#Nominal_level" rel="nofollow noreferrer">nominal variable</a> and make &quot;Unknown&quot; its own category, but then I won't be able to leverage the order information (can't have my cake and eat it too).</li>
  </ol>
  <p><strong>QUESTION:</strong>  Are there any other mathemagical options for this Goldie Locks?</p>
  <p>By the way, I know I can try all of these options and see what works.  That is not what I'm looking for.  I'm really looking to see if there are better options that I've not considered yet.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-11-17T11:04:25.340Z
Comments.Text: A way is to build a model to predict the class (or the encoded class value) of the unknown Grade first, then use the predicted grade to build the desired prediction model.
Comments.UserDisplayName: lpounng
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-11-17T11:45:21.840Z
Comments.Text: @lpounng, what you describe is essentially multivariate imputation.  The link in the question post leads to an article that describes their version of it (which they call the IterativeImputer), as follows:  "It does so in an iterated round-robin fashion: at each step, a feature column is designated as output y and the other feature columns are treated as inputs X. A regressor is fit on (X, y) for known y. Then, the regressor is used to predict the missing values of y."
Comments.UserDisplayName: Snehal Patel
Tags: <data-preprocessing><data-science>
Title: How to handle missing data for an ordinal feature variable for a deep learning model?
UserDisplayName: Snehal Patel
ThreadText: # How to handle missing data for an ordinal feature variable for a deep learning model?
  
  (Asked by: username_0 on 2022-11-17)
  
  BACKGROUND:  I am developing a deep learning model in which one feature variable (out of many) is the grade of cancer, an ordinal variable.  Below is a breakdown of my data by grade:
  GRADE       SAMPLES
  --------------------
  Grade I     746
  Grade II    1128
  Grade III   2908
  Grade IV    1909
  Unknown     4255
  
  PROBLEM:  I need a solution to handle a large amount of missing data.
  OPTIONS I HAVE CONSIDERED:
  
  Because of the amount of missing data, just removing those study subjects is not an option (can't remove rows -- too many).
  Because grade is an important predictor variable, removing grade as a feature is also not an option (can't remove the column -- too important).
  I fear that imputation is not going to give me accurate results since so much of my data is missing.  I have considered multivariate imputation methods as well, but I'm still not convinced I can get meaningful results.  Maybe I'm wrong.
  I can just treat grade like a nominal variable and make "Unknown" its own category, but then I won't be able to leverage the order information (can't have my cake and eat it too).
  
  QUESTION:  Are there any other mathemagical options for this Goldie Locks?
  By the way, I know I can try all of these options and see what works.  That is not what I'm looking for.  I'm really looking to see if there are better options that I've not considered yet.
  
  **Tags:** data-preprocessing, data-science
  > **Comments:**
  > **username_1**: A way is to build a model to predict the class (or the encoded class value) of the unknown Grade first, then use the predicted grade to build the desired prediction model.  
  > **username_0**: @lpounng, what you describe is essentially multivariate imputation.  The link in the question post leads to an article that describes their version of it (which they call the IterativeImputer), as follows:  "It does so in an iterated round-robin fashion: at each step, a feature column is designated as output y and the other feature columns are treated as inputs X. A regressor is fit on (X, y) for known y. Then, the regressor is used to predict the missing values of y."  
  
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  ## 0 answers
  
  