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.

Why is it important to test a model on unseen data?

• To improve the data splitting process

• To reduce the model complexity

• To increase the training data size

• To ensure the model is not overfitting

What is the purpose of the 'in model' option in PROC LOGISTIC?

• To read a previously saved model for making predictions

• To split the data into training and validation sets

• To save the model for future use

• To calculate the misclassification rate

What does the 'fit stat' option provide when using PROC LOGISTIC?

• The predicted probabilities

• The classification and misclassification rates

• The ROC curve

• The data splitting ratio

What is the ROC curve used for in model evaluation?

• To compare the model's performance against a baseline

• To determine the data splitting ratio

• To calculate the misclassification rate

• To save the model for future use

What does a significant ROC contrast test result indicate?

• The model performs similarly to the baseline

• The model needs more training data

• The model is overfitting

• The model is better than the reference model