We will discuss supervised learning. Supervised means to oversee or direct a certain activity and make sure it is done correctly. In this type of learning the machine learns under guidance. So, at school or teachers guided us and taught us similarly in supervised learning machines learn by feeding them label data and explicitly telling them that this is the input and this is exactly how the output must look. So, the teacher, in this case, is the training data.
Supervised Learning is a type of machine learning wherein we teach the machine using label data. So, input and output are the labels.
Types of Problems
The type of problems that are solved using supervised learning. So, under supervised learning, we have two main categories of problems. We have regression problems and we have classification problems. There is an important difference between classification and regression. Basically, classification is about predicting a label or a class whereas regression is about predicting a continuous quantity.
Now let’s say that you have to classify your emails into two different routes. So here basically we’ll be labeling our emails as spam and non-spam emails. For this kind of problem where we have to assign our input data into different classes. We make use of classification algorithms. On the other hand, regression is used to predict a continuous quantity now a continuous variable is a variable that has an infinite number of possibilities. So, regression is a predictive analysis used to predict continuous variables.
You don’t have to label data in two different classes. Instead, you have to predict a final outcome. Let’s say that you want to predict the price of a stock over a period. For such problems, you can make use of regression algorithms.
Type of Data
The type of data used to train a machine. When it comes to supervised learning. It’s quite clear and simple the machine will be provided with a label set of input and output data in the training phase itself. So basically, you feed the output of your algorithm into the system. This means that in supervised learning, the machine already knows the output of the algorithm before it starts working on it. Now an example is classifying a data set into either cats or dogs. So, if the algorithm is fed an image of a cat, the image is labeled as a cat, similarly for a dog. This is how the model is taught. It’s told that this is a cat by labeling it. After the algorithm is taught it is then tested using a new data set. But a point to remember here is that in the training phase for supervised learning the input is also labeled and the output is also labeled.
The next parameter to consider is training. In supervised learning, the training phase is well defined and very explicit. The machine is fed training data where both the input and output are labeled and the only thing the algorithm has to do is map the input to the output. So, the training data act like a teacher or a guide over here now once the algorithm is well trained. It is tested using new data.
The main aim or the end goal of a supervised learning algorithm is to forecast an outcome. That is the basic aim of all these machine learning types. But the whole supervised learning process is built in such a way that it can directly give you a predicted outcome because supervised learning algorithms have a very well-defined training phase.
The approach followed when it comes to supervised learning is quite simple, like I mentioned earlier all that the algorithm has to do is map the known input to the known output.
In supervised learning, there is a direct feedback mechanism since the machine is trained with build input and output.
Some of the popular algorithms supervised learning has are linear regression, which is mainly used for regression problems. It also has algorithms like support vector machines, decision trees and so on and these can also be used for classification problems.
Applications of supervised learning are widely used in the business sector for forecasting risks, risk analysis, Predicting sales profit and so on.
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