Andrea Coombs, Senior Systems Engineer, JMP
You have a question to answer, so you collect the relevant data and are ready to start creating a predictive model. But what which type of model do you choose and which modeling type? Is the goal to segment, predict, explain, or identify? Are the variables continuous, nominal, or ordinal?
Before we even get to choosing a type of model, we must define how the variables are used in analysis with the modeling type. What happens if we treat number of bedrooms as continuous versus a nominal or ordinal modeling type? We see when we pull up a distribution how modeling type impacts the type of summary statistics we get as means or frequencies. This poster demonstrates how the modeling type determines the results of your predictive model depending on which type of model is chosen. Get ready to play “Name that Analysis” as we go head-to-head on classifying different case study modeling examples with their respective modeling type.
