A classification task can be evaluated in many different ways to achieve specific objectives. Of course, the most important metric is the accuracy, often expressed as:
In scikit-learn, it can be assessed using the built-in accuracy_score() function:
from sklearn.metrics import accuracy_score>>> accuracy_score(Y_test, lr.predict(X_test))0.94399999999999995
Another very common approach is based on zero-one loss function, which we saw in Chapter 2, Important Elements in Machine Learning, which is defined as the normalized average of L0/1 (where 1 is assigned to misclassifications) over all samples. In the following example, ...
