5.10 Logistic Polynomial Regression, Bayes Decision Boundaries, and k-fold Cross Validation

Estimated decision boundaries of polynomial logistic regression models for simulated data are shown. The Bayes decision boundary is the dashed purple line.

Figure 5.5: Estimated decision boundaries of polynomial logistic regression models for simulated data are shown. The Bayes decision boundary is the dashed purple line.

In practice, the true test error and Bayes error rate are unknown, so we need to estimate the test error rate.
This can be done via k-fold cross-validation, which is often a good estimate of the true test error rate.

The test error rate (beige), the training error rate (blue), and the 10-fold cross validation error rate (black) are shown for polynomial logistic regression and KNN classification.

Figure 5.6: The test error rate (beige), the training error rate (blue), and the 10-fold cross validation error rate (black) are shown for polynomial logistic regression and KNN classification.