Looking at different performance evaluation metrics
In the previous sections and chapters, we evaluated different machine learning models using prediction accuracy, which is a useful metric with which to quantify the performance of a model in general. However, there are several other performance metrics that can be used to measure a model’s relevance, such as precision, recall, the F1 score, and Matthews correlation coefficient (MCC).
Reading a confusion matrix
Before we get into the details of different scoring metrics, let’s take a look at a confusion matrix, a matrix that lays out the performance of a learning algorithm.
A confusion matrix is simply a square matrix that reports the counts of the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) predictions of a classifier, as shown in Figure 6.9:
Figure 6.9: The confusion matrix
Although these metrics can be easily computed manually by comparing the actual...
