Supervised and Unsupervised: What’s the difference?

Author(s): Eashan Mahajan

Originally published on Towards AI.

With machine learning’s surge of popularity in the past few years, more and more people spend hours each day trying to learn as much as they can. The field attracts avid learners, with companies using machine learning to make their tasks easier. With the use of machine learning, people find out about the 2 main types of machine learning: Supervised and Unsupervised learning.

We’re going to cover what both types are and when exactly you should use them. Let’s get right into it.

Supervised Learning

First, what exactly is supervised learning? It is the most common type of machine learning that you will use. In supervised machine learning, the machine learning algorithm is trained on a labeled dataset. Each example within the dataset is labeled with an expected output or target value.

For the algorithm to utilize supervised learning, the dataset has to list the target value for each example within the dataset. During training and testing, you would remove the column that has the correct values, and after the model has completed the training phase, you would check the values the model outputted with the correct values.

However, the issue with these target values is that often datasets won’t contain target values. Public datasets are often disorganized and don't contain results. More often than not, you’re going to have to hire an expert to label each example with an accurate target value.

Example: Say that you were recently hired by a finance company. They give you a dataset that contains information about their clients and their history with loans. Within the dataset, they’ve already given you the target values (max amount of money a customer can receive from a loan). They want you to create a machine learning model that can accurately predict how much of a loan future customers should receive depending on their history.

This is where supervised learning would come in handy. Your algorithm would analyze the labeled dataset by studying each piece of information provided for each example within the dataset. From there, you’ll train and test the model and present a final product to your company.

Alright, the pros and cons.

Pros:

1. It’ll be very easy to judge whether or not the model performed poorly or well
2. You know exactly what you need to look for
3. Supervised learning algorithms are very efficient — given properly labelled data and a lot of it
4. Supervised learning is very versatile — you can apply it to several applications

Cons:

1. The time required for supervised learning is immense, much more than unsupervised
2. Pre-processing the data can take weeks, depending on the size of the dataset
3. Its quite easy to overfit on supervised learning algorithms

In the end, it’ll come down to your scenario. Supervised learning can be extremely effective if used in the right situation.

Unsupervised Learning

Unsupervised learning, alongside supervised learning, is one of the main types of machine learning. Unsupervised machine learning is generally used for clustering data. Essentially, its a type of algorithm that learns from unlabeled data. The data doesn’t have the correct answers for each example, which is the opposite of supervised learning.

Since it is essentially given no instructions, the model will act of its own accord, analyzing the data by itself. It will attempt to find the hidden pattern within the data without any additional help. The model will be unable to evaluate the fitness of a candidate function. Instead, the algorithm will attempt to find functions that can be changed into simialr clusters. This way, the changed clusters will have examples that are more similar to other examples within the cluster, compared to examples in other clusters.

Example: Imagine that you possess a dataset that contains information about customers that use an online retail store. This data consists of things such as their purchases, age, where they bought the products, etc. When you use unsupervised learning on this, you can apply an algorithm that will group up the customers based on similarities within their data when compared to other customers. This allows for the company to target customers based on certain purchases or their behavior, allowing for more targeted advertisements.

Pros:

1. Unsupervised learning can reveal hidden patterns that you may not have noticed. It’ll could allow for a deeper analysis of the data and more thoughtful insights.
2. You won’t need to spend time or money on finding labeled data. This allows for a wider range of datasets to be used for your model.
3. Unsupervised learning is very flexible, allowing you to adapt and change it to match whatever you need for most projects.

Cons:

1. Often, the results from an unsupervised learning algorithm will be difficult to decipher, requiring you to spend more time especially when you’re dealing with complex data or patterns.
2. While the model will find patterns on its own, the limited guidance it has could result in the model providing horrible results or outcomes.
3. Since unsupervised learning tasks don’t have a clearly defined objective, evaluating the results of such a task can be challenging as there are several opinions on it.

Conclusion

So, which one should you use? The answer: both. Both supervised and unsupervised learning have their strengths and weaknesses. They have their applications where they will thrive, providing you with optimal results. In other cases, they won’t be as successful, resulting in suboptimal cases. I highly recommend working with both types, as you’ll undoubtedly keep coming back to them. For now, that’s all I’ve got for you, and thanks for reading!

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Published via Towards AI