What is the first step of the predictor grouping process?

The first step of the predictor grouping process is model development. During model development, various predictors are created and tested to determine their effectiveness in predicting outcomes. This step is crucial as it lays the foundational framework for the subsequent stages of the predictor grouping process. The outcomes from this phase guide the selection and refinement of predictors, ensuring that the models built are robust and grounded in the right data.

In this initial phase, data scientists and analysts focus on identifying relevant variables, transforming them as needed, and constructing preliminary models to examine how well these predictors perform individually and in combination. This proactive approach sets the stage for later actions, such as model analysis, where the performance of these models is comprehensively assessed.

The other stages listed, such as scoring comparison and data analysis, occur after model development. Scoring comparison evaluates the performance of various models, while data analysis focuses on understanding the underlying data characteristics. Therefore, starting with model development is essential for establishing a solid predictive framework that can be fine-tuned in later stages.