Supervised Learning simplified
Supervised Learning simplified

A brief overview of supervised learning

In this article, I want to give you a brief overview of supervised learning, where it came from and what are the main applications of supervised learning today. Supervised learning is a subset of Machine Learning in Artificial Intelligence. supervised learning refers to the application of supervised learning to optimize a certain piece of data or a system. It can also be defined as a supervised learning technique, which makes use of various supervised learning techniques such as supervised labeling, supervised recognition, supervised analysis, etc. supervised learning can be used for all sorts of supervised data manipulation like learning complex algorithms, developing language/grammar/reading comprehension or any other supervised form of learning.

Origin of Supervised Learning

The origin of supervised learning is actually much deeper than what is presented here. However, the basic concept was described elegantly by John McCarthy in his paper “supervised learning in machine learning” in 1969. This paper has become very popular especially for professors and higher education students. In this paper, he presented some models for supervised learning and then explained what exactly supervised learning algorithm is.

How does supervised learning work?

How does supervised learning work? supervised learning works by making use of some well-defined set of rules or a supervised learning algorithm that decides which outputs should be given to which inputs. For instance, in the real world we could say that all students in a high school should be allowed to take part in sports. And for that to happen, the school administration would decide who among the students should be allowed to participate in sports programs and how many would be allowed to take part. Now, the supervised learning algorithm is the one that takes that into consideration and decides what output should be given to what input.

Basically, what happens in the real-life situation is that each student learns in a different subject differently from the others. If we want to apply supervised learning in Machine Learning, what we do is that we take all those individual differences and blend them together to form one big learning system, or we could say that we apply a supervised learning algorithm that learns a single model for all possible inputs and conditions under which the inputs will be given. That is basically what supervised learning is all about.

Supervised learning in machine learning
Machine Learning concept. Chart with keywords and icons on white desk with stationery

Limitation of supervised learning

On the downside, supervised learning is rather slow when it comes to training a model because it needs lots of data to be processed. Also, in some cases it can be frustrating as it makes it harder for you to make your model generalize over all possible situations. Also, supervised learning requires a lot of data mining because all the learning is done with the help of lots of data which must be processed manually, making the whole process rather time-consuming. Also, supervised learning algorithm tends to be fairly generic in its approach as it doesn’t require any information on the domain in which it was trained.

This is where a supervised learning algorithm comes into play. When you use one of these learning methods, what you do is that you feed the machine a number of inputs and it learns how to perform a certain task. For example, if you feed a model for a picture, then it should be able to recognize what it is and what it should look like. The advantage of this type of learning is that the models can be made generic over all possible inputs. It is also a more generic approach than what we have used earlier – the supervised learning where we restricted our training only to a particular domain.

Another thing that makes supervised learning very popular is that the supervised learning algorithm gets to make generalization errors. In other words, it tries to generalize from each input into every possible output. The drawback of this is that it can lead to false predictions. But if you are using a good supervised learning algorithm, you shouldn’t have such problems. For instance, in the Image classification algorithm there is an example where it predicts what the cat face recognition algorithm will do next.

Another problem with supervised learning is that sometimes it leads to overfitting or over-fitting. When the supervised learning algorithm starts to see patterns in the data, it tends to generalize too much. And when it generalizes too much it doesn’t guarantee that the pattern it saw was truly a real result. However, overfitting and underfitting are not big issues when using image recognition because the whole point here is to help you save time by allowing the machine to make educated guesses.


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