Confusion Matrix
A confusion matrix describes the performance of the classification model. In other words, a confusion matrix is a way to summarize classifier performance. The following table shows a basic representation of a confusion matrix and represents how the predicted results by the model compared to the true values:
Figure 6.3: Basic representation of a confusion matrix
Let's go over the meanings of the abbreviations that were used in the preceding table:
- TN (True negative): This is the count of outcomes that were originally negative and were predicted negative.
- FP (False positive): This is the count of outcomes that were originally negative but were predicted positive. This error is also called a type 1 error.
- FN (False negative): This is the count of outcomes that were originally positive but were predicted negative. This error is also called a type 2 error.
- TP (True positive): This is the count of outcomes that were originally...
