How can the 'k-distance graph' be used in selecting the optimal Epsilon for DBSCAN?
- By calculating the average distance to k-nearest neighbors
- By determining the distance between k centroids
- By displaying k clusters' distances
- By plotting the distance to the kth nearest neighbor of each point
The 'k-distance graph' can be used to select the optimal Epsilon by plotting the distance to the kth nearest neighbor for each point and looking for an "elbow" or a point of inflection. This inflection point can be a good estimate for Epsilon, helping to choose a value that balances density requirements without overly segmenting the data.
Loading...
Related Quiz
- Which learning paradigm does not require labeled data and finds hidden patterns in the data?
- You are working with a dataset containing many irrelevant features. Which regularization technique would you prefer and why?
- In a scenario with noisy data, increasing the value of 'k' in the k-NN algorithm can help to ________ the noise.
- When an agent overly focuses on actions that have previously yielded rewards without exploring new possibilities, it might fall into a ________ trap.
- What is the risk of using the same data for both training and testing in a Machine Learning model?