D2L - Chapter 3.6 Generalization

3.6 Generalization

1. Exploring both what is known about the principles underlying generalization in various models
2. Also heuristic techniques that have been found (empirically) to yield improved generalization on tasks of practical interest.

3.6.1 Training Error and Generalization Error

3.6.1.1 Model Complexity

3.6.2 Underfitting or Overfitting

1. Underfitting, both error high
2. Overfitting, training error low, validation error high

3.6.2.1 Polynomial Curve Fitting

3.6.2.2 Dataset Size

3.6.3 Model Selection

3.6.3.1 Cross-validation

1. split into k
2. train on k - 1, validate on the remaining
3. average among all the errors

3.6.4 Summary