Is DBSCAN sensitive to the choice of Epsilon and MinPts? Why or why not?
- No, they are auto-calculated parameters
- No, they have minimal effect on the outcome
- Yes, they define the shape of the clusters
- Yes, they influence the density of clusters
DBSCAN is indeed sensitive to the choice of Epsilon and MinPts. These parameters are crucial in determining the density of the clusters, as Epsilon controls the maximum radius of the neighborhood, and MinPts sets the minimum number of points required to form a dense region. Selecting inappropriate values can lead to suboptimal clustering results.
Loading...
Related Quiz
- In which algorithm is the outcome determined based on a majority vote from its neighbors?
- You have a dataset with numerous features, and you suspect that many of them are correlated. Using which technique can you both reduce the dimensionality and tackle multicollinearity?
- Which variant of RNN is designed to better capture long-term dependencies in sequence data?
- An educational institution wants to personalize its online learning platform for individual student needs. How would you leverage Machine Learning to achieve this goal?
- Support Vector Machines (SVM) aim to find a ______ that best divides a dataset into classes.