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Supervised and Unsupervised Machine learning

Updated: Apr 13, 2020

Supervised Learning:-


The process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.Here, All data is labeled and the algorithms learn to predict the output from the input data.

For example:-

You are a kid, you see different types of animals, your father tells you that this particular animal is a dog…after him giving you tips few times, you see a new type of dog that you never saw before - you identify it as a dog and not as a cat or a monkey or a potato.

This is supervised learning as you have a teacher to guide you and learn things such that when a new data comes your way you have an idea about it and you may categorize it.

Types of supervised learning:-


Classification: A classification problem is when the output variable is a category, such as “red” or “green”

Regression: A regression problem is when the output variable is a real value, such as “weight”.


Unsupervised Learning :-


It is called unsupervised because there is no correct answers and there is no teacher. Algorithms are left to their own to discover and present the interesting structure in the data.All data is unlabeled and the algorithms learn to inherent structure from the input data.

For example:-

You go bag-packing to a new country, you did not know much about it - their food, culture, language etc. However from day 1, you start making sense there, learning to eat new cuisines including what not to eat, find a way to that beach etc.

This is unsupervised learning as you have no teacher to guide you and you yourself learn from the patterns and comes to an outcome that makes sense to u.

Types of unsupervised learning:-


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.


Author: Shruthi Agarwal


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