Complete SAS Programming Guide - Learn SAS and Become a Data Ninja - Feature Engineering

The video tutorial discusses feature engineering, emphasizing its role in improving model discrimination and calibration. It explains how to create new features like total income and EMI, and the importance of normalizing data and dropping correlated features. The tutorial provides code examples and demonstrates improvements in model performance, particularly in terms of the area under the curve and calibration plots.

Why is feature engineering included in the evaluation metrics module?

• Because it simplifies the model

• Because it is an evaluation metric

• Because it can improve model discrimination and calibration

• Because it is a coding technique

What is the purpose of creating the 'total income' feature?

• To assess the applicant's credit score

• To provide a more predictive variable for the response

• To determine the applicant's savings

• To calculate the applicant's expenses

How is the EMI feature calculated?

• By subtracting the loan term from the loan amount

• By adding the loan amount and loan term

• By dividing the loan amount by the loan term

• By multiplying the loan amount with the interest rate

Why is the log of total income taken?

• To decrease the value of the feature

• To normalize the right-skewed distribution

• To increase the value of the feature

• To make the distribution left-skewed

What is the effect of dropping highly correlated features?

• It increases the complexity of the model

• It reduces the model's accuracy

• It prevents multicollinearity issues

• It has no effect on the model