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 by taking the log of skewed distributions. The tutorial also covers dropping highly correlated features to enhance model performance. Finally, it demonstrates the improvements in model validation, including better area under the curve and calibration plots.

Why is feature engineering placed under the evaluation metrics module?

• Because it is a type of evaluation metric.

• Because it can enhance model discrimination and calibration.

• Because it is unrelated to model performance.

• Because it simplifies the coding process.

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

• To simplify the data collection process.

• To increase the complexity of the model.

• To reduce the number of features.

• To improve the model's prediction of loan repayment ability.

How is the EMI feature calculated?

• By subtracting the loan term from the loan amount.

• By multiplying the loan amount by the interest rate.

• By dividing the loan amount by the loan term.

• By adding the loan amount and loan term.

Why is the log transformation applied to the total income feature?

• To reduce its importance in the model.

• To make it more complex.

• To normalize its right-skewed distribution.

• To increase the feature's variance.

What is the effect of dropping highly correlated features on the model?

• It has no effect on the model.

• It decreases the model's accuracy.

• It increases the model's complexity.

• It improves the model's performance by reducing multicollinearity.