Let’s start off this blog on Supervised Learning vs Unsupervised Learning vs Reinforcement Learning by taking a small real-life example.
Imagine, you have to assemble a table and a chair, which you bought from an online store. How will you go about it? Well, obviously, you will check out the instruction manual given to you, right? You will follow the instructions in it and build the whole set. Otherwise, if you don’t have the instruction manual, you will have to figure out how to build the table-and-chair set.
This scenario is similar to Machine Learning. With a set of data available and a motive present, a programmer will be able to choose how he can train the algorithm using a particular learning model. There are three types of machine learning which are, supervised, unsupervised, and reinforcement learning. Let’s talk about each of these in detail and try to figure out the best learning algorithm among them. Further in this blog, let’s look at the difference between supervised, unsupervised, and reinforcement learning models.
But, before that, let’s see what is supervised and unsupervised learning individually.
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Supervised Learning: What is it?
Consider yourself as a student sitting in a math class wherein your teacher is supervising you on how you’re solving a problem or whether you’re doing it correctly or not. This situation is similar to what a supervised learning algorithm follows, i.e., with input provided as a labeled dataset, a model can learn from it. Labeled dataset means, for each dataset given, an answer or solution to it is given as well. This would help the model in learning and hence providing the result of the problem easily.
So, a labeled dataset of animal images would tell the model whether an image is of a dog, a cat, etc.. Using which, a model gets training, and so, whenever a new image comes up to the model, it can compare that image with the labeled dataset for predicting the correct label.
How do you think supervised learning is useful? Let’s talk about that next!
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Why is Supervised Learning useful? Types of Problems
Next, let’s see whether supervised learning useful or not.
Well, to make you understand that let me introduce to you the types of problems that supervised learning deals with. There are two types of problems: classification problems and regression problems.
Classification problems ask the algorithm to predict a discrete value that can identify the input data as a member of a particular class or group. Taking up the animal photos dataset, each photo has been labeled as a dog, a cat, etc., and then the algorithm has to classify the new images into any of these labeled categories.
Regression problems are responsible for continuous data, e.g., for predicting the price of a piece of land in a city, given the area, location, etc.. Here, the input is sent to the machine for predicting the price according to previous instances. And the machine determines a function that would map the pairs. If it is unable to provide accurate results, backward propagation is used to repeat the whole function until it receives satisfactory results.
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Next, let’s talk about unsupervised learning before you go ahead into understanding the difference between supervised and unsupervised learning.
It is important to understand about Unsupervised Learning before, we learn about Supervised Learning vs Unsupervised Learning vs Reinforcement Learning.
Unsupervised Learning: What is it?
As you saw, in supervised learning, the dataset is properly labeled, meaning, a set of data is provided to train the algorithm. The major difference between supervised and unsupervised learning is that there is no complete and clean labeled dataset in unsupervised learning.
Confused? Well, let me explain it to you in a better way.
Unsupervised learning is a type of self-organized learning that helps find previously unknown patterns in data set without pre-existing labels.
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So, can we use Unsupervised Learning in practical scenarios? Let’s talk about that next before looking at Supervised Learning vs Unsupervised Learning vs Reinforcement Learning!
How is Unsupervised Learning used?
When you are talking about unsupervised learning algorithms, a model receives a dataset without providing any instructions. Also, you don’t know exactly what you need to get from the model as an output yet. You might be guessing that there is some kind of relationship between the data within the dataset you have, but the problem here is that the data is too complex for guessing. What will the model do then? Well, in such cases grouping of data is done and comparison is made by the model to guess the output.
Consider the animal photo example used in supervised learning. Suppose, there is no labeled dataset provided. Then, how can the model find out if an animal is a cat or a dog or a bird? Well, if the model has been provided some information such as if an animal has feathers, a beak, wings, etc. it is a bird. In the same way, if an animal has fluffy fur, floppy ears, a curly tail, and maybe some spots, it is a dog, and so on.
Hence, according to this information, the model can distinguish the animals successfully. But, if it is not able to do so correctly, the model follows backward propagation for reconsidering the image.
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Difference Between Supervised and Unsupervised Learning
Now that you have enough knowledge about both supervised and unsupervised learning, let’s look at the difference between supervised and unsupervised learning in tabular form now:
Reinforcement Learning: What is it?
After discussing on supervised and unsupervised learning models, now, let me explain to you reinforcement learning. As it is based on neither supervised learning nor unsupervised learning, what is it? To be straight forward, in reinforcement learning, algorithms learn to react to an environment on their own.
To be a little more specific, reinforcement learning is a type of learning that is based on interaction with the environment. It is rapidly growing, along with producing a huge variety of learning algorithms that can be used for various applications.
To begin with, there is always a start and an end state for an agent (the AI-driven system); however, there might be different paths for reaching the end state, like a maze. This is the scenario wherein reinforcement learning is able to find a solution for a problem. Examples of reinforcement learning include self-navigating vacuum cleaners, driverless cars, scheduling of elevators, etc.
Let’s understand reinforcement learning in detail by looking at the simple example coming up next.
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Reinforcement Learning: How does it work?
Consider an example of a child trying to take his/her first steps. What will be the instructions he/she follows to start walking?
- Observing others walking and trying to replicate the same
- Standing still
- Remaining still
- Trying to balance the body weight, along with deciding on which foot to advance first to start walking.It sounds like a difficult and challenging task for a child to get up and walk, right? But for us, it is easy since we have become used to it over time.
Now, putting it together, a child is an agent who is trying to manipulate the environment (surface or floor) by trying to walk and going from one state to another (taking a step). A child gets a reward when he/she takes a few steps (appreciation) but will not receive any reward or appreciation if he/she is unable to walk. This is a simplified description of a reinforcement learning problem.
I hope this example explained to you the major difference between reinforcement learning and other models. However, let’s go ahead and talk more about the difference between supervised, unsupervised, and reinforcement learning.
Supervised vs Unsupervised vs Reinforcement Learning
Finally, now that you are well aware of Supervised, Unsupervised, and Reinforcement learning algorithms, let’s look at the difference between supervised unsupervised and reinforcement learning!
In a nutshell, supervised learning is when a model learns from a labeled dataset with guidance. And, unsupervised learning is where the machine is given training based on unlabeled data without any guidance. Whereas reinforcement learning is when a machine or an agent interacts with its environment, performs actions, and learns by a trial-and-error method.
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