The SEMMA framework is a systematic approach for data mining and predictive modeling developed by SAS. It consists of five main stages: Sample, Explore, Modify, Model, and Assess. The process involves selecting a sample of the data, analyzing its characteristics and relationships, preparing the data for modeling, building and evaluating predictive models, and finally, evaluating the overall performance of the models.
SEMMA Data Framework
Goals
To have a representative sample of the data to work with
To understand the characteristics and relationships in the data
To prepare the data for modeling by cleaning, transforming, and feature engineering
To build and evaluate predictive models with good performance
To identify areas for improvement in the models and data preparation process
Best pratices
Start with a large enough sample of the data to ensure representativeness.
Take the time to thoroughly explore the data to gain insights and identify potential issues.
Clean, transform, and engineer features in a way that maximizes the predictive power of the data.
Use a variety of modeling techniques to find the best-performing model.
Regularly assess the performance of the models to identify areas for improvement and make necessary adjustments.