## 6.15 Assessing Model Accuracy

• There are various measure of model accuracy (depend on outcome)
• If outcome binary we can use the below
• Training error rate: the proportion of mistakes that are made if we apply estimate to the training observations
• $$\frac{1}{n}\sum_{i=1}^{n}I(y_{i}\neq\hat{y}_{i})$$: Fraction of incorrect classific
• $$\hat{y}_{i}$$: predicted class label for observation $$i$$
• $$I(y_{i}\neq\hat{y}_{i})$$: indicator variable that equals 1 if $$y_{i}\neq\hat{y}_{i}$$ and zero $$y_{i}=\hat{y}_{i}$$
• If $$I(y_{i}\neq\hat{y}_{i})=0$$ then the ith observation was classified correctly (otherwise missclassified)
• Test error rate: Associated with a set of test observations of the form ($$x_{0},y_{0}$$)
• $$Ave(I(y_{0}=\hat{y}_{0}))$$
• $$\hat{y}_{0}$$: predicted class label that results from applying the classifier to the test observation with predictor $$x_{0}$$
• Good classifier: One for which the test error is smallest
• The opposite of the error rate is the Correct Classification Rate (CCR)
• How many were correctly classified?
• Source: James et al. (2013 Chap. 2.2.3)

### References

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics. Springer.