Lecture 17 – Bias and Variance
Presented by Fernando Perez and Ani Adhikari
Content by Fernando Perez, Ani Adhikari, Suraj Rampure
Important: You may want to review Lecture 3 for a refresher on random variables.
A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.
Variance of random variables. Walking through an alternate calculation of variance. Variance of a linear transformation.
Deriving the variance of a sum. Understanding covariance, correlation, and independence.
Variance of an i.i.d. sum. Variance of the Bernoulli and binomial distributions.
Variability of the sample mean. Reviewing inferential concepts from Data 8, but with the framework of random variables.
Introducing the data generating process and prediction error. Model risk.
Looking at different sources of error in our model – observation variance, model variance, and bias – and discussing how to mitigate them.
Decomposing model risk into the sum of observation variance, model variance, and the square of bias.