Chapter 5 Reading

Today, I read Chapter 5 (5.1 5.2) of the book.

Summary

Quantitative Measures of Performance
RMSE: root mean squared error (take the square root of MSE so as to have the same units as the original data)
Coefficient of determination (R^2): the proportion of the information in the data that is explained by the model. It is a measure of correlation, not accuracy. It is dependent on the variation in the outcome. Same RMSE, larger variance, larger R^2.
Spearman’s rank correlation: the rank of the observed and predicted outcomes are obtained and the correlation coefficient between these ranks is calculated.

The Variance-Bias Trade-off
E[MSE]=σ^2+〖(Model Bias)〗^2+Model Variance
σ^2: residual variance, also called ‘irreducible noise’. ‘Model Bias’ reflects how close the functional form of the model can get to the true relationship between the predictors and the outcome.
It is generally true that more complex models can have very high variance, which leads to over-fitting.
The Variance-Bias Trade-off: increase the bias in the model to greatly reduce the model variance as a way to mitigate the problem of collinearity.

Tomorrow, I will do computing of Chapter 5.