Body: <p>I have a dataset which has users (rows) with the list of their interests (IABs), which looks like this</p>
  <pre><code>user_id | gender | list of interests
  --------+--------+--------------------------------
  user 1  | male   | games, productivity
  user 2  | female | games, lifestyle, design
  user 3  | male   | travel, games, messaging
  user 4  | male   | messaging, blogging, lifestyle
  ...
  </code></pre>
  <p>Since the number of unique interests are few (~500) and the number of rows are high (~67M), what are the feature engineering practices that I should follow to get an ML model score a better accuracy?</p>
  <p>P.S.: Simple model with one hot/count hot vectorization yields an accuracy of ~52%</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-03-11T18:03:08.597Z
Comments.Text: Your title suggests a question about metric but your question is asking about feature engineering for fixed metric (accuracy). Please update either the title or question
Comments.UserDisplayName: SajanGohil
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-03-11T18:09:11.117Z
Comments.Text: @SajanGohil updated the title
Comments.UserDisplayName: theodre7
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-03-13T15:50:27.730Z
Comments.Text: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Comments.UserDisplayName: Community
Tags: <machine-learning><classification><feature-selection><feature-engineering>
Title: Best feature engineering approach for interest-based age classification
UserDisplayName: theodre7
ThreadText: # Best feature engineering approach for interest-based age classification
  
  (Asked by: username_0 on 2023-03-11)
  
  I have a dataset which has users (rows) with the list of their interests (IABs), which looks like this
  user_id | gender | list of interests
  --------+--------+--------------------------------
  user 1  | male   | games, productivity
  user 2  | female | games, lifestyle, design
  user 3  | male   | travel, games, messaging
  user 4  | male   | messaging, blogging, lifestyle
  ...
  
  Since the number of unique interests are few (~500) and the number of rows are high (~67M), what are the feature engineering practices that I should follow to get an ML model score a better accuracy?
  P.S.: Simple model with one hot/count hot vectorization yields an accuracy of ~52%
  
  **Tags:** machine-learning, classification, feature-selection, feature-engineering
  > **Comments:**
  > **username_1**: Your title suggests a question about metric but your question is asking about feature engineering for fixed metric (accuracy). Please update either the title or question  
  > **username_0**: @SajanGohil updated the title  
  > **username_2**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
  
  ---
  
  ## 0 answers
  
  