Assessing smallest clusters
In this exercise you're going to have a look at the clusters that came out of DBSCAN, and flag certain clusters as fraud:
- you first need to figure out how big the clusters are, and filter out the smallest
- then, you're going to take the smallest ones and flag those as fraud
- last, you'll check with the original labels whether this does actually do a good job in detecting fraud.
Available are the DBSCAN model predictions, so n_clusters is available as well as the cluster labels, which are saved under pred_labels. Let's give it a try!
This exercise is part of the course
Fraud Detection in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Count observations in each cluster number
counts = np.bincount(____[____ >= 0])
# Print the result
print(counts)