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I recently studied about supervised learning and unsupervised learning. From theory, I know that supervised means getting the information from labeled datasets and unsupervised means clustering the data without any labels given.
But, the problem is I always get confused to identify whether the given example is supervised learning or unsupervised learning during my studies.
Can anyone please give a real life example?
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One practical example of supervised learning problems is predicting house prices. How is this achieved? First, we need data about the houses: square footage, number of rooms, features, whether a house has a garden or not, and so on. We then need to know the prices of these houses, i.e. the corresponding labels.
An example of unsupervised machine learning would be a case where a supermarket wants to increase its revenue. It decides to implement a machine learning algorithm on its sold products' data. It was observed that the customers who bought cereals more often tend to buy milk or those who buy eggs tend to buy bacon.
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Supervised Learning:
Example:
Classification: Machine is trained to classify something into some class.
- classifying whether a patient has disease or not
- classifying whether an email is spam or not
Regression: Machine is trained to predict some value like price, weight or height.
- predicting house/property price
- predicting stock market price
Unsupervised Learning:
Example:
Clustering: A clustering problem is where you want to discover the inherent groupings in the data
- such as grouping customers by purchasing behavior
Association: An association rule learning problem is where you want to discover rules that describe large portions of your data
- such as people that buy X also tend to buy Y
Read more: Supervised and Unsupervised Machine Learning Algorithms
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