Colorado Technical University Machine Learning Algorithm Evaluation Paper

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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 Na? Bayes) used today?  
  • What are two common issues that can arise with the use of each? 

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Machine Learning Algorithm Evaluation

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Machine Learning Algorithm Evaluation
Differentiate between the three common Unsupervised Machine Learning Algorithms (Kmeans, DBSCAN, and hierarchal). When should they be used?

Unsupervised learning is a machine learning technique that utilizes models that are not
supervised by training datasets. Rather, the models find hidden configurations and acumens from
the readily available data (Sravanan & Sujatha, 2018). Unsupervised learning is quite important
as it works on unlabeled and uncategorized data and intends to find new insights from the
provided data. Additionally, unsupervised learning works similarly to human learning and thus
making it closer to artificial intelligence. K-means clustering, one of the algorithms in machine
learning, is a vector quantization technique...

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