Supervised vs. unsupervised learning: Which is best for you?

Data Science, AI and ML
, , ,
#1

Your data scientists’ assessment of the structure and volume of your data, as well as the use case, will determine the best approach for your situation.

Make the following considerations while making your decision:

Examine the information you’ve provided:

Is the data labeled or unlabeled?
Do you have any professionals who can help with more labeling?

Define your objectives:

Do you have a well-defined, reoccurring problem to solve?
Or will the algorithm have to anticipate new issues?

Examine your algorithm options:

Is it possible to find algorithms with the same dimensionality (number of features, traits, or characteristics) as yours?
Can they handle the volume and structure of your data?

Classifying large amounts of data might be difficult in supervised learning, but the results are highly accurate and reliable. On the other hand, unsupervised learning can handle massive amounts of data in real-time. However, there is a lack of transparency into how data is clustered, which increases the danger of incorrect outcomes.

This is where the concept of semi-supervised learning comes into play.