Practical Data Science using Python - Linear Regression Model Evaluation and Optimization

The video tutorial covers feature selection techniques for optimizing linear regression models. It begins with dropping features based on probability values, then checks multicollinearity using Variance Inflation Factor (VIF). Recursive Feature Elimination (RFE) is introduced for automatic feature selection. The tutorial concludes with model testing, validation, and performance evaluation, achieving an R-squared score of 86% for the test data.

What does an adjusted R-squared value indicate about a model's performance?

What is the significance of dropping columns with a P value greater than 0.05?

Explain the purpose of the Variance Inflation Factor (VIF) in model optimization.

What does a high VIF value indicate about a feature in a regression model?

What is the impact of dropping too many features from a model?

Describe the process of Recursive Feature Elimination (RFE) and its benefits.

Why is it important to check for residual normality in regression analysis?

How can you ensure that the test data set matches the features used in the final model?

What steps are involved in preparing the test data set for model prediction?

How do you interpret the results of the R-squared score in model evaluation?