Differentiate between the 3 common unsupervised machine learning algorithms (K-means, DBSCAN, and hierarchal). When should they be used?

Primary Response: Within the Discussion Board area, write 300–500 words that respond to the following questions with your thoughts, ideas, and comments. This will be the foundation for future discussions by your classmates. Be substantive and clear, and use examples to reinforce your ideas.

For this Discussion Board, please complete the following:

Supervised learning allows you to collect data from a previous experience such as weather conditions during tornado season for a particular area. This data collection is valuable as it contribute to emergency preparedness. Unsupervised learning can be further understood by the case of the baby and the family dog. The baby becomes familiar with the family dog and understands its’ features such as ears, nose, tail and other attributes. When the baby is introduced to other dogs, the learning from the family dog applies and allow the baby to understand that new animal is a dog too.

• Differentiate between the 3 common unsupervised machine learning algorithms (K-means, DBSCAN, and hierarchal). When should they be used?

• How are the common types of supervised machine learning algorithms (decision trees, random forest, neural networks, and Naive Bayes) used today?

• What are two common issues that can arise with the use of each?