How does SAS Enterprise Miner determine the success of predictive models?

SAS Enterprise Miner measures the success of predictive models primarily by evaluating support and confidence metrics. These metrics are particularly relevant in the context of association rules and transaction data, where support indicates how frequently items appear together in the data set, and confidence provides a measure of the strength of the association between the items. This approach is vital in understanding how well the model predicts outcomes based on historical patterns.

In predictive modeling, success is often gauged not just by theoretical constructs but by practical implications that support and confidence offer, highlighting how reliably the model can forecast business outcomes based on discovered patterns. Thus, using these metrics contributes significantly to assessing the validity and predictive power of the models developed within SAS Enterprise Miner.

Options involving cluster analysis, correlation of variables, or assessment of business objectives serve different purposes in data analysis or model interpretation. While they are important aspects of the overall modeling and analysis process, they do not directly provide the specific metrics necessary for evaluating the predictive success of the model as effectively as support and confidence do.