Database Reference
In-Depth Information
Table 7.7
Confusion Matrix of Naïve Bayes from the Bank Marketing Example
Predicted Class
Total
Subscribe Not Subscribed
Actual Class
Subscribed
3 8
11
Not Subscribed 2
87 89
Total
5 95
100
The
accuracy
(or the
overall success rate
) is a metric defining the rate at
which a model has classified the records correctly. It is defined as the sum of TP
and TN divided by the total number of instances, as shown in
Equation 7.18
.
A good model should have a high accuracy score, but having a high accuracy score
alone does not guarantee the model is well established. The following measures can
be introduced to better evaluate the performance of a classifier.
As seen in Chapter 6, the
true positive rate
(TPR) shows what percent of
positive instances the classifier correctly identified. It's also illustrated in
Equation
The
false positive rate
(FPR) shows what percent of negatives the classifier
marked as positive. The FPR is also called the
false alarm rate
or the
type I
error rate
and is shown in Equation
7.20
.
The
false negative rate
(FNR) shows what percent of positives the classifier
marked as negatives. It is also known as the
miss rate
or
type II error rate
and
