What is Supervised Learning?
As the name suggests, Supervised Learning is the process of learning under someone’s Supervision or Guidance. Just like you were taught by your teacher in School/ College. Similarly, in terms of Machine Learning, Supervised Learning is the process, in which we teach or train a machine to do some specific task using, set of DATA. This DATA will have all the information ( All the questions, answers), which is required for that task. After that Machine is provided with a new Set of DATA (Questions), Machine’s task is now to analyze, this new data and make predictions / provide solutions ( based on what it learned from training DATA).
Check out this image below:
Suppose, you are given with a bowl of fruits and you have to train the machine to determine the fruit type and name.
So, How will you do …
First, you will feed all the data to machine one by one, so that it can determine which is what …
For eg :
1) If Color is RED, Shape is round and having little depth at the top, then the object is labeled as Apple
2) If Color is Yellow-green, Shape is curvy, then the object is labeled as Banana
3) If the Color is Violet/Black/ Purple, Shape is round and Small, then the object is labeled Black Grapes.
Similarly, you will provide the machine, all the data, Now after that Machine will analyze this and will remember it. The data you above provided will be called as Training Sets. And the above process is called Training.
Now, Once the training is Completed, you will provide Machine a different bowl of fruits or a single fruit, in order to test – What Machine has learned so far.
Now the machine’s task is to determine which fruit is this and how it will do? — It will just remember all the data you previously provided during Training and based on that, it will Classify this as Banana.
Thus the machine learns the things from training data(basket containing fruits) and then apply the knowledge to test data(new fruit).
Supervised learning is further classified into two categories of algorithms:
Classification: Machine is trained to classify something into some class.
For eg: classifying whether a patient has a disease or not
classifying whether an email is spam or not
or like in above example : Machine determined fruit type.
Regression: Machine is trained to predict some value like price, weight or height.
For eg: predicting house/property price
predicting stock market price