Description
Some machine learning models are essentially decision rules with if-then-else constructs. Distillation of this knowledge into rulelists and rulesets provides an interpretable overview of the decision-making process. Explainability leads to clear idea about interventions, explanation to outliers and many more use-cases. We present a few hands-on use cases with 'imodels' (python package for rule based models) and 'tidyrules' (R package for ruleset manipulation and post-hoc reordering and pruning) along with utilities to convert the rulesets into SQL to bring them into production setting.
Instructor's Bio
Srikanth K S
Director, Data Science at Games24x7
Data Science Professional – A leader with hands-on technical expertise - Data Science, Causal inference, Explainable AI and model interpretability, Predictive modeling, Machine learning, Deep learning, Artificial Intelligence, recommender systems with a background in Applied mathematics, Statistics and Optimization. - At Walmart: Established disciplines as a data science leader, created data science pipelines, built models at scale in Retail areas such as Merchandising, Assortment, Personalization, Advertising platform, Supply-chain, Forecasting and Transportation alongside working with multiple stakeholders, cross-functional teams. Managed a team of data scientists, UI/UX developers, ML engineers and DevOps engineers.
Webinar
-
1
ON-DEMAND WEBINAR: "Machine Learning Models To Interpretable Rules"
-
Ai+ Training
-
Webinar recording
-
UPCOMING LIVE TRAINING
Register now to save 30%
