In the previous sections and chapters, we evaluated our models using the model accuracy, which is a useful metric 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, and the F1-score.
Before we get into the details of different scoring metrics, let's print a so-called confusion matrix, a matrix that lays out the performance of a learning algorithm. The confusion matrix is simply a square matrix that reports the counts of the true positive, true negative, false positive, and false negative predictions of a classifier, as shown in the following figure:
Although these metrics can be easily computed manually by comparing the true and predicted class labels, scikit-learn provides a convenient confusion_matrix function that we can use as follows:
>>> from sklearn.metrics import confusion_matrix...
