Actionable Rules from Machine Learning Models
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
- Brief on how rules based models create rules
- Examples of models and demonstration of rule extraction
- Brief understanding of support-confidence-lift paradigm
- Interpreting and choosing rules
- Using rule based systems in production
- 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.
- Drive actionable insights from your data.
- Facilitate stakeholders in data-driven decision making process.
- Craft better features.
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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