Diagnosing Regularization Bias vs. Variance

Diagnosing Bias vs. Variance

This means:

• High bias = under-fitting = $J_{train}(\theta)$ high and $J_{cv}(\theta)$ high
• High variance = over-fitting = $J_{train}(\theta)$ low and $J_{cv}(\theta)$ high

Regularization: help prevent overfitting

This is the next step once we chose the degree of polynomial for our dataset

This means: 

• Large $\lambda$ = High bias = $J_{train}(\theta)$ high and $J_{cv}(\theta)$ high
• Small $\lambda$ = High variance = $J_{train}(\theta)$ low and $J_{cv}(\theta)$ high

Steps:

Resources:

- https://www.coursera.org/learn/machine-learning/supplement/81vp0/diagnosing-bias-vs-variance

- https://www.coursera.org/learn/machine-learning/supplement/JPJJj/regularization-and-bias-variance