Supervised vs. Unsupervised Machine Learning: Which is best for you?

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Choosing the right approach for your situation depends on how your data scientists assess the structure and volume of your data, as well as the use case. To make your decision, be sure to do the following:

  • Evaluate your input data: Is it labeled or unlabeled data? Do you have experts that can support additional labeling?
  • Define your goals: Do you have a recurring, well-defined problem to solve? Or will the algorithm need to predict new problems?
  • Review your options for algorithms: Are there algorithms with the same dimensionality you need (number of features, attributes or characteristics)? Can they support your data volume and structure?

Classifying big data can be a real challenge in supervised learning, but the results are highly accurate and trustworthy. In contrast, unsupervised learning can handle large volumes of data in real time. But, there’s a lack of transparency into how data is clustered and a higher risk of inaccurate results. This is where semi-supervised learning comes in.

key differences between supervised and unsupervised AI learning:

  • Goals: In supervised learning, the goal is to predict outcomes for new data. You know upfront the type of results to expect. With an unsupervised learning algorithm, the goal is to get insights from large volumes of new data. The machine learning itself determines what is different or interesting from the dataset.
  • Applications: Supervised learning models are ideal for spam detection, sentiment analysis, weather forecasting, and pricing predictions, among other things. In contrast, unsupervised learning is a great fit for anomaly detection, recommendation engines, customer personas, and medical imaging.
  • Complexity: Supervised learning is a simple method for machine learning, typically calculated through the use of programs like R or Python. In unsupervised learning, you need powerful tools for working with large amounts of unclassified data. Unsupervised learning models are computationally complex because they need a large training set to produce intended outcomes.
  • Drawbacks: Supervised learning models can be time-consuming to train, and the labels for input and output variables require expertise. Meanwhile, unsupervised learning methods can have wildly inaccurate results unless you have human intervention to validate the output variables.