Chapter 5&6(6.1 6.2) application

Today, I read Chapter 5&6(6.1 6.2) to find something useful which could be used in my research.

Summary

Chapter 5 Application

Quantitative measures of performance

RMSE: the square root of MSE so that it is in the same units as the original data

: the proportion of the information in the data that is explained by the model. It is a measure of correlation, not accuracy.

The variance-bias trade-off: high variance or high bias, that is a question. Increasing bias can reduce model variance.

Chapter 6 Application

Ordinary linear regression finds parameter estimates that have minimum bias, whereas ridge regression, the lasso, and the elastic net find estimates that have lower variance.

Linear regression:

SSE: sum-of-squared errors.

When there are highly correlated predictors, we should remove one of the offending predictors or find models which can tolerate collinearity.

Tomorrow, I will read more of the book to frame questions.