Lesson 2
Revisiting the Machine Learning Pipeline
Chapter : Lesson 2
Episode 7 - Train and Test Distributions
Summary:
- You can consider the scores/errors on the training or test sets.
- High training error:
- Possibly underfitting (high bias)
- Bias can be seen as the training error
- Low training error, high test error:
- Possibly overfitting (high variance)
- Variance can be seen as the difference between the training error and the test error.
- Aim for low training and test error (the ‘sweet spot’ - low bias and low variance).