Supervised Learning in Machine Learning

supervised learning is a learning in which we train the machine using data which is well labeled (i.e the questions with answers). After that, the machine is provided with a new set of questions(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.

For instance, suppose you are given an basket filled with different kinds of fruits. Now the first step is to train the machine with all different fruits one by one like this:

• If shape of object is rounded and depression at top having color Red then it will be labelled as –Apple.
• If shape of object is long curving cylinder having color Green-Yellow then it will be labelled as –Banana.

Now suppose after training the data, you have given a new separate fruit say Banana from basket and asked to identify it

Since the machine has already learned the things from previous data and this time have to use it wisely. It will first classify the fruit with its shape and color and would confirm the fruit name as BANANA and put it in Banana category. Thus the machine learns the things from training data(basket containing fruits) and then apply the knowledge to test data(new fruit).

Supervised learning classified into two categories of algorithms:

• Classification: A classification problem is when the output variable is a category, such as “Red” or “blue” or “disease” and “no disease”.
• Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.