Reminder of performance metrics
Remember the credit dataset? With all the extra knowledge you now have about metrics, let's have another look at how good a random forest is on this dataset. You have already trained your classifier and obtained your confusion matrix on the test data. The test data and the results are available to you as tp
, fp
, fn
and tn
, for true positives, false positives, false negatives, and true negatives respectively. You also have the ground truth labels for the test data, y_test
and the predicted labels, preds
. The functions f1_score()
and precision_score()
have also been imported.
This exercise is part of the course
Designing Machine Learning Workflows in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
print(____(____, preds))