Supervised learning is where observations contain input-output pair, just like when you're at a 1
school and you want to learn something. 2
Teacher is usually giving you some data. 3
Suppose this is a math problem. 4
It's giving you a match problem with the solution. 5
And then after a few times after, let's say several times, then a teacher will take a quiz or we'll 6
take a test by giving you the question itself and not giving the answer. 7
And you are required to write down your answer based on the data that have been used to train you. 8
It's just looking the same. 9
We have some data with 80 percent of this data. 10
We will train the network with the remaining 20 percent. 11
We do the testing. 12
Supervised learning, it's also the simplest one to understand in supervised algorithms, 13
You may not know the inner relations of the data you are processing, but of course you do know very 14
well which is the output you need from your model. 15
For example, I need to be able to start predicting when users will cancel their subscription. 16
Suppose this is a Netflix company or YouTube channel and they are looking to keep their users in subscribe. 17
They want to have a method to predict which users are most likely going to unsubscribe for the 18
upcoming months. 19
For this purpose, they have some users history and they know which users unsubscribed and which users 20
based on their activity to just stayed subscribed. 21
Let's say, for example, we have here 10000 data. 22
These are users' history, users usages' (activity) history; from these, 23
Maybe five thousand of them already canceled their subscription. 24
And from this 10000, 5000, are still staying on their subscription. 25
What you can do is take data from this set who unsubscribed their subscription after a few months. 26
Let's take four thousand five hundred of this data and give it to a machine for training purposes. 27
And from these sets of data, the users, which are very good users, they just stayed subscribed to their 28
product. 29
Let's take another four thousand five hundred to train 30
the network. We will show this data to our machine, to our system, and we will tell the system this is 31
the user history. 32
These users unsubscribed the product, but these are the good users who stayed subscribed and they didn't 33
unsubscribe their products after a learning process, 34
it's time to test the data. From these nine thousand data, we have the remaining 1000 from this (group of) one thousand, 35
We are going to take a quiz of our system. We randomly, give the data to the system and this time 36
we are not giving the result. 37
We are not telling the machine if this user stayed subscribed or already unsubscribed their product and we will 38
check if it can already predict the correct subscribe or subscribe status for each user. 39
This is just one example of how can we use this supervised learning.