Unsupervised learning is a type of machine learning algorithm that doesn’t require ______? *labels A. labels B. images C. objects D. variables

The DBSCAN algorithm works by utilizing a combination of two parameters: _____, and _____? *min_points, epsilon A. min_points, size B. min_dist, range C. min_density, epsilon D. min_radius, epsilon

With the DBSCAN algorithm, a point is considered a core point if it has at least N__ points within its epsilon neighborhood? *N A. L B. M C. N D. O

The DBSCAN algorithm requires two parameters. Name any one of them. *min_points A. min_points B. epsilon C. noise D. density

DBSCAN can be used to detect ________? *anomalies A. clusters B. correlations C. outliers D. anomalies

DBSCAN performs poorly in which of the following situations? *A large number of overlapping clusters A. A small number of distinct clusters B. A large number of overlapping clusters C. Large variations in the density of clusters D. A large number of outliers

What is the primary benefit of using DBSCAN? *It’s able to identify clusters of arbitrary shape A. It’s easy to implement B. It’s able to identify clusters of arbitrary shape C. It’s able to identify outliers D. It’s able to identify correlations

With the DBSCAN algorithm, a point is assigned to a cluster if it has a minimum number of points in its ε_ neighborhood? *ε A. ẟ B. η C. ε D. ζ

DBSCAN is an unsupervised learning algorithm that can identify patterns in data based on _______? *density A. similarity B. size C. density D. shape

DBSCAN is useful for clustering data because it does not assume that clusters are of any _______? *particular shape A. particular size B. particular shape C. particular color D. particular distance