Supervised vs Unsupervised Learning

In supervised learning, you train the model with a set of input data and a corresponding set of paired labeled output data. The labeling is typically done manually.

Unsupervised machine learning is when you give the algorithm input data without any labeled output data. Then, on its own, the algorithm identifies patterns and relationships in and between the data

Semi-supervised learning is when you apply both supervised and unsupervised learning techniques to a common problem

You can apply semi-supervised learning when it’s difficult to obtain labels for a dataset. You might have a smaller volume of labeled data but a significant amount of unlabeled data. Compared to using the labeled dataset alone, you’d have greater accuracy and efficiency if you combine supervised and unsupervised learning techniques.

	Supervised learning	Unsupervised learning
What is it?	You train the model with a set of input data and a corresponding set of paired labeled output data.	You train the model to discover hidden patterns in unlabeled data.
Techniques	Logistic regression, linear regression, decision tree, and neural network.	Clustering, association rule learning, probability density, and dimensionality reduction.
Goal	Predict an output based on known inputs.	Identify valuable relationship information between input data points. This can then be applied to new input to draw similar insights.
Approach	Minimize the error between predicted outputs and true labels.	Find patterns, similarities, or anomalies within the data.

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