from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix

These are two Python functions that are part of the scikit-learn library and are used for evaluating the performance of a classification model.

The classification_report function generates a report that includes precision, recall, f1-score, and support for each class in the classification model. The precision is the ratio of true positives to the total number of predicted positives, while recall is the ratio of true positives to the total number of actual positives. The f1-score is the harmonic mean of precision and recall, and the support is the number of samples in each class.

The confusion_matrix function, on the other hand, creates a matrix that shows the number of true positives, false positives, true negatives, and false negatives for each class in the classification model. This is useful for identifying which classes the model is performing well on and which ones need improvement, as well as for identifying any patterns in misclassifications.