Beyond predictions, some ML models provide rules to identify actionable sub-populations in support-confidence-lift paradigm. Along with making the models interpretable, rules make it easy for stakeholders to decide on the plan of action. We discuss rule-based models in production, rule-based ensembles, anchors using R package: tidyrules.

 
 

Outline/Structure of the Demonstration

  1. Brief on how rules based models create rules
  2. Examples of models and demonstration of rule extraction
  3. Brief understanding of support-confidence-lift paradigm
  4. Interpreting and choosing rules
  5. Using rule based systems in production
  6. Rule ensembles and anchors
  • a. (1-5 mins) Introduce global vs local interpretation of a ML model and when each of them is useful.
  • b. (6-12 mins) Introduce rules from decision trees based models with a demonstration.
  • c. (13-19 mins) Present an implemented use-case.

Learning Outcome

  1. Drive actionable insights from your data.
  2. Facilitate stakeholders in data-driven decision making process.
  3. Craft better features.

Target Audience

Those interested in interpretable ML, actionable insights

Prerequisites for Attendees

Basic understanding of tree-based models

schedule Submitted 1 year ago

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      References:

      1. "https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers":https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers
      2. "https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429":https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429
      3. "https://www.bloomberg.com/news/articles/2016-09-21/spotify-is-perfecting-the-art-of-the-playlist":https://www.bloomberg.com/news/articles/2016-09-21/spotify-is-perfecting-the-art-of-the-playlist
      4. "https://dl.acm.org/citation.cfm?id=1864770":https://dl.acm.org/citation.cfm?id=1864770
      5. "Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modelling": https://arxiv.org/pdf/1810.12027.pdf
      6. "Deep Reinforcement Learning for Page-wise Recommendations": https://arxiv.org/pdf/1805.02343.pdf
      7. "Deep Reinforcement Learning for List-wise Recommendations": https://arxiv.org/pdf/1801.00209.pdf
      8. "Deep Reinforcement Learning Based RecSys Using Distributed Q Table": http://www.ieomsociety.org/ieom2020/papers/274.pdf
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