Practical Data Science using Python - Linear Regression Model Optimization

The video tutorial covers the process of plotting actual and predicted Y values using scatter plots, highlighting the importance of a linear pattern to detect anomalies. It explains residual normality through probability plots and discusses the need for further investigation if the plot deviates from the expected line. The tutorial also covers testing for homoscedasticity using scatter plots of fitted and residual values, ensuring constant variance. Finally, it evaluates the model's performance using adjusted R-squared values, achieving 92% for training and 86.5% for testing, indicating a decent model fit.

How is homoscedasticity tested in the context of linear regression?

What do the adjusted R-squared values indicate about the model's performance?