Understanding Supervised Learning in Machine Learning

Supervised learning is one of the algorithmic methods used in machine learning. Have you ever heard of supervised learning or machine learning?

Through this article, my web will provide complete information about supervised learning and also other information related to the algorithm. However, before entering into a further discussion regarding supervised learning, let’s first understand what machine learning is.

What is Machine Learning?

The field of AI (Artificial Intelligence) technology continues to develop rapidly, especially in machine learning or machine learning. So, what is machine learning?

Machine learning is an algorithm that is inserted into a machine so that it can carry out learning independently, without any help from humans. Machine learning has the ability to mine data, study data, and produce output into several actions based on the data obtained.

The concept of algorithms machine learning has been put forward since the 1920s by mathematical scientists, namely Thomas Bayer, Adrien Marie Legendre, and Andrey Markov. Those who put forward and develop machine learning models.

In human life today, the presence of machine learning of course has enormous benefits. Machine learning can learn what users need, what interests them, and so on.

Then, how can machine learning carry out independent learning? This is where we will enter into the opening discussion of supervised learning or directed learning.

Machine learning has 2 basic independent learning techniques, namely supervised learning and unsupervised learning. These two basic learning techniques have different learning methods so that their application is based on the learning needs of the machine learning itself.

For example, everyone definitely has a different way of learning. There are people who can understand learning topics just by reading, there are those who have to listen to learning material in auditory form, and there are also people who have to rewrite it.

The difference in the way each person learns also occurs in learning machine learning. So, supervised learning and unsupervised learning are 2 different ways of learning in machine learning.

What is Supervised Learning?

After understanding what machine learning is, now let’s get into the main topic of discussion, namely the meaning of directed learning. In machine learning, the learning process is carried out under the supervision or presence of a supervisor called supervised learning.

What is the meaning of the supervised learning process? Isn’t machine learning an AI that can carry out the learning process independently?

In developing algorithms using directed learning learning techniques, there is a model that is used as a benchmark to obtain a high level of accuracy. When machine learning wants to learn something new and provide output, the algorithm will compare it with the model that has been created and this is what is meant by the learning method of supervised learning.

For example

you often order food via the online application. The majority of the food you order is a menu that provides seafood such as fried rice seafood , green mussels with Padang sauce, crab meatballs, and others.

From the foods that you often order, the online application that you use will provide various recommendations for new restaurants that you have never tried before. It is certain that the recommended restaurant has a menu that serves seafood on its menu.

When we put the example into directed learning, the entire output produced in machine learning< /span> produced will have a higher level of accuracy.output will be adapted to the model already made. The more data that is entered into the model, the

If we talk from a technical perspective, supervised learning has two variables, namely X as an input variable and Y as a variable output. The formula for mapping algorithm variables supervised learning is Y = f(X).

From this formula, we can see that the goal to be obtained from directed learning is to understand the mapping function of variable X to be used as output on variable Y. The data entered as variable X can predict the results that will be produced by variable Y.

What is the Difference Between Supervised Learning and Unsupervised Learning?

Machine learning has 2 ways of learning, namely supervised learning and unsupervised learning. You have learned what supervised learning is and now we will see what the differences are between these two learning processes.

Unsupervised learning is a learning process that focuses on data exploration. This algorithm does not require models or supervision like directed learning

Machine learning which uses an algorithm unsupervised learning can study data and look for patterns. These patterns will then be classified into certain categories to facilitate the output results from machine learning< a i=7> in the future.

Unsupervised learning does not have a model or target variable so the mapping formula is just f(X). This is because returning to the focus of the learning carried out is to explore and group data.

In conclusion, the difference between supervised learning and unsupervised learning This can be seen from whether there is a target variable Y or not. In addition, unsupervised learning does not require the model used in training data whereas supervised learning requires a model to be able to provide the correct output results.

Leave a Comment