Feature Selection and Modeling Techniques

The Feature Selection and Modeling session in the MMM Advanced Workshop focuses on building strong, interpretable models through structured variable selection and effective modeling techniques. It emphasizes the importance of identifying causal and relevant predictors while avoiding common statistical pitfalls.

Participants learn Aryma Labs’ Trifecta Approach to feature selection, which combines:

• Transfer Entropy (TE): Captures both linear and non-linear dependencies to identify causally relevant variables.

• Granger Causality (GC): Determines if past values of one variable can predict another, useful in understanding time-lagged effects.

• Domain Expertise: Validates variable relevance based on business context and common sense.

This structured selection process ensures that the final model inputs are not only statistically significant but also interpretable and business-aligned.

The session also critiques stepwise regression, highlighting its statistical limitations and the risk of misleading variable importance. Instead, it promotes a more controlled modeling framework using Frequentist linear regression, chosen for its transparency, simplicity, and practical interpretability in marketing contexts.

Participants will explore:

• The case for identifying a single best model rather than relying on automated ensembles.

• Handling variable transformations (e.g., adstocking media inputs).

• Interpreting regression coefficients meaningfully within the marketing mix framework.

By the end of the session, learners will understand how to select features systematically, build more reliable models, and communicate insights effectively for media planning and business decision-making.