Supervised and unsupervised learning

Supervised and unsupervised learning

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  supervised and unsupervised learning Submittedby- Paras Kohli B.Tech (CSE)
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  Supervised learning • Supervisedlearning: suppose you had a basket and it is fulled with some fresh fruits your task is to arrange the same type fruits at one place. • suppose the fruits are apple,banana,cherry,grape. • so you already know from your previous work that, the shape of each and every fruit so it is easy to arrange the same type of fruits at one place. • here your previous work is called as train data in data mining. • so you already learn the things from your train data, This is because of you have a response variable which says you that if some fruit have so and so features it is grape, like that for each and every fruit. • This type of data you will get from the train data. • This type of learning is called as supervised learning. • This type solving problem come under Classification. • So you already learn the things so you can do your job confidently.
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  Unsupervised learning • supposeyou had a basket and it is fulled with some fresh fruits your task is to arrange the same type fruits at one place. • This time you don't know any thing about that fruits, you are first time seeing these fruits so how will you arrange the same type of fruits. • What you will do first you take on fruit and you will select any physical character of that particular fruit. suppose you taken colours. • Then the groups will be some thing like this. • RED COLOR GROUP: apples & cherry fruits. GREEN COLOR AND SMALL SIZE: grapes. • This type of learning is know unsupervised learning.
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  Aim Of SupervisedLearning • The aim of supervised, machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. As adaptive algorithms identify patterns in data, a computer "learns" from the observations. When exposed to more observations, the computer improves its predictive performance.
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  Example • suppose youwant to predict whether someone will have a heart attack within a year. You have a set of data on previous patients, including age, weight, height, blood pressure, etc. You know whether the previous patients had heart attacks within a year of their measurements. So, the problem is combining all the existing data into a model that can predict whether a new person will have a heart attack within a year.
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