Difference Between Supervised And Unsupervised Learning

Updated 9 July 2022

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With the world becoming smarter every day, supervised and unsupervised learning are the two most common and basic machine learning techniques. These approaches have distinct nuances and features that outperform one technique over another. It is important for you to understand these differences if you aspire to have a career in machine learning or artificial intelligence. In this article, we define the two techniques and discuss the difference between supervised and unsupervised learning.

Difference between supervised and unsupervised learning

Below are the major differences between supervised and unsupervised learning:

Training data: Regarding the training data used in the two types of learnings, supervised learning uses labelled data while unsupervised learning uses unlabelled data. Labelled data is well defined and comes with tags, whereas unlabelled data is not well defined and does not come with tags.
Feedback: In supervised learning, the model takes direct feedback, whereas, in unsupervised learning, the model does not take any direct or indirect feedback. The supervised learning models use feedback to check if the predicted output is correct or not.
Output: In supervised learning, the models predict the output. In unsupervised learning, the models do not predict the output; they find hidden patterns in the data.
Input: Supervised learning requires input data and the output to the model. In unsupervised learning, you only feed input data to the model with no output.
End goal: In supervised learning, the model predicts the output for any new data. In unsupervised learning, the goal is that the model finds hidden patterns in the data.
Supervision required: True to its name, supervised learning models require supervision during training. In unsupervised learning, the model does not require any supervision during training.
Categorisation: The two main categories in supervised learning are classification and regression, whereas in unsupervised learning the main categories are clustering and associations.
Use cases: Supervised learning finds use in those cases where the input and output correspond to the available input. Unsupervised learning is useful in those cases where only the input is available and you do not have the corresponding output.
Results accuracy: Supervised learning produces highly accurate results, whereas unsupervised learning produces less accurate results in comparison to supervised learning.
Close to artificial intelligence: You cannot consider supervised learning to be very close to artificial intelligence as the model predicts the output only after getting trained for each set of data. In unsupervised learning, the model learns on its own.
Computational complexity: Supervised learning is highly complex computationally. Unsupervised learning is comparatively less complex.
Number of classes: In supervised learning, you know the number of classes, whereas you do not know the number of classes in unsupervised learning.
Algorithms: Supervised learning includes algorithms like linear regression, support vector machine, logistic regression, multi-class classification, Bayesian logic, decision tree, etc. Unsupervised learning includes algorithms like clustering, Apriori algorithm and KNN.

Supervised learning

Supervised learning is a machine learning technique in which you train models by using labelled data. In this technique, you provide both the input data and the output corresponding to that input data to determine the output. The model then learns based on the input provided and predicts results once it has learned sufficiently.

This technique requires supervision in terms of telling the model the expected output for any input. The results provided by models in this technique are highly accurate.

Example of supervised learning

Suppose you have images of different types of vegetables. When you provide these images to a supervised learning model as input, you also provide the output to the model in terms of the identity of vegetables based on different factors, like shape, size and colour. After learning the identity of different vegetables based on these factors and sufficient training, the model predicts the identity of the vegetable using the correct algorithm.

Unsupervised learning

Unsupervised learning is a machine learning technique in which you train the model using unlabelled data. In this technique, you provide only the input data to the model. The model then works on the dataset provided to find hidden patterns in the data. In unsupervised learning, the model learns like a child; through experience, without explicitly being told what the expected output is for a particular input.

Since you do not tell the model the expected output for any particular input, this model requires no supervision. At the same time, the results provided by models in this technique are less accurate when compared to supervised learning.

Example of unsupervised learning

In the example given above, for the different vegetables, if you do not give the model the output in terms of the identity of vegetables based on different factors like shape, size, colour etc., the model tries and works out the identity of the vegetables using a suitable algorithm on its own. Here, you do not train the model to provide the expected output, but the model works intelligently.

Reinforcement learning

Reinforcement learning is another prominent technique of machine learning. Here, the models work to ensure maximum rewards in any given situation through a sequence of steps. In this technique, you provide input to the model, which is an initial state for the learning to start. Unlike supervised learning, there are many different outputs possible for each input. The users, then, train the model by rewarding or punishing the model for each output state it reaches.

In this sequential learning, the users reward the model at each stage if they deem the output to be correct. The model learns by aiming to earn the maximum rewards. After the learning continues for a sufficient time, the model works out the best reward based on the path that earns the maximum reward. There are two types of reinforcement learnings:

Positive reinforcement

In positive reinforcement, for any event that you want to be the output, the model is told that this particular behaviour is desirable. You convey this through rewards for the model and then the model understands this and increases the frequency of that behaviour. Essentially, this has a positive impact on the behaviour of the model.

Negative reinforcement

In negative reinforcement, for any event that you do not want to be the output, the model is instructed that this particular behaviour is not desirable. You convey this to the model through punishment so that it stops this behaviour in the future. This reinforcement has a negative impact in terms of ensuring that the model avoids undesirable behaviours.

Choosing between supervised and unsupervised learning

The choice of learning method depends on many factors. Below are some of these factors that you may consider before making the decision:

Data available

If you have unlabelled data available for training your model, you may choose to use unsupervised learning. If labelled data is available, you may use supervised learning. Understanding the type of data available to you helps in making the correct decision in terms of the learning method to be used.

Output desired

If you want your model to predict the output, then you may use supervised learning. If you expect your model to find the hidden patterns in the data, then unsupervised learning would be the best option. Having this clarity in terms of the output required helps you make the right decision.

Availability of output corresponding to the input

If both the input data and the corresponding output are available, you may choose supervised learning. If only the input data is available and the corresponding output is unavailable, unsupervised learning would be more suitable. This clarity is important to use the correct learning method.

Accuracy of results desired

If you desire a high level of accuracy, then supervised learning would be the most suitable choice. If the accuracy of the output is not that important, then you may opt for unsupervised learning. Taking the complexity of the calculations into account is equally important when looking at the accuracy required.

Supervision available

Supervised learning models require supervision during training. So if supervision is available, you may choose to go with supervised learning. In unsupervised learning, the model does not require any supervision during training. So, if supervision is not available, you may use unsupervised learning.