Complete SAS Programming Guide - Learn SAS and Become a Data Ninja - Scoring Validation Dataset Using Code

The video tutorial explains how to make predictions using a model and evaluate its performance on unseen data. It covers the use of PROC LOGISTIC to read a saved model and generate predictions, followed by assessing the model's accuracy through misclassification rates. The tutorial also demonstrates how to analyze the ROC curve to compare the model's predictive power against a baseline model with no predictive capability. The results show that the model performs well on unseen data, similar to other models tested earlier.

What is the purpose of making a prediction in the context of model evaluation?

Explain the significance of the validation set in model training.

What does the 'proc logistic' command do in the context of this model?

How can the misclassification rate be interpreted in model evaluation?

What does the ROC curve indicate about the model's performance?