Supervised Learning vs Unsupervised Learning vs Reinforcement Learning

Machine learning is a subset of artificial intelligence, that powers various applications by enabling machines to learn from data and make decisions without explicit programming. 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 learn in detail the difference between supervised, unsupervised, and reinforcement learning models.

Table of Contents

• What is Supervised Learning?
• What is Unsupervised Learning?
• What is Reinforcement Learning?
• Key Differences Between Supervised vs Unsupervised Learning vs Reinforcement Learning
• Supervised Learning Use Cases, Types, and Algorithms
• Unsupervised Learning Use Cases and Algorithms
• Choosing the Right Algorithm

What is Supervised Learning?

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 that for each dataset given, an answer or solution to it is given as well. This would help the model in learning and hence provide 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 this, 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.

What is Unsupervised Learning?

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 better.

Unsupervised learning is a type of self-organized learning that helps find previously unknown patterns in data sets without pre-existing labels.

What is Reinforcement Learning?

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 Machine Learning applications.

Key Differences Between Supervised vs Unsupervised Learning vs Reinforcement Learning

Here are the following differences between  supervised vs Unsupervised Learning and Reinforcement Learning:

Supervised Learning Use Cases, Types, and Algorithms

Supervised Learning use cases

Here are some of the most common use cases of Supervised Learning:

• Text Classification: it involves suspecting spam emails, helps in language detection, also trains the chatbot to learn the user’s intents.
• Image Classification: it involves identifying the image, and extracting text inside the image or any documents.
• Natural language processing: it involves performing NLP tasks like: Identifying entities such as names, dates, and locations in text and training models to translate languages or summarize text using labeled examples. etc.
• Object detection in videos and images: it helps in Identifying traffic signs, vehicles, and obstacles to enable safe navigation, Detecting unauthorized access, identifying individuals, or tracking objects in video footage.
• Medical Diagnosis: it helps us to classify medical test results (e.g., blood tests, imaging scans) to predict diseases such as cancer, diabetes, or heart conditions,
• Fraud detection: it helps in detecting suspicious transactions in banking, credit card usage, or online payments.

Types of Problems in Supervised Learning

There are two types of problems: classification problems and regression problems.

• Classification 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: 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.

Supervised Learning algorithms

There are multiple supervised learning algorithms used in machine learning some of them are:

• Linear Regression
• Decision Trees
• Logistic Regression
• Random Forest
• Support Vector Machines (SVM)
• k-Nearest Neighbors (kNN)
• Gradient Boosting
• Naive Bayes algorithm

Unsupervised Learning Use Cases and Algorithms

Unsupervised Learning use cases

Here are the following use cases of Unsupervised Learning:

• Clustering:  It means grouping similar data into clusters based on their features. This can be used in customer segmentation, image segmentation, and recommendation engines.
• Dimensionality reduction: It reduces the number of features in a dataset while retaining as much information as possible. This can help to simplify the modeling problem, improve the performance of a learning algorithm, or make it easier to visualize the data.
• Anomaly detection: It identifies rare events, items, or observations that are suspicious because they differ significantly from standard behaviors.
• Generative models:It creates new data models that are similar to the input data.

Unsupervised Learning algorithms

There are multiple unsupervised learning algorithms used these days some of them are:

• Principal component analysis (PCA)
• Hierarchical clustering
• t-SNE ( t-Distributed Stochastic Neighbor Embedding)
• Association rules
• K-Means

Reinforcement Learning Use cases and Algorithms

Reinforcement Learning use cases

Here are the following use cases of reinforcement  Learning:

• Application in self-driving cars: it is used in self-driving cars, to navigate the traffic, and make safe and better decisions.
• Robotics: it helps in training robots to perform tasks like: moving, speaking etc.
• Game playing: it trains gents in playing games like: chess on a Pro level.
• Industry automation: It helps to automate tasks in Industial field as well.
• Trading and finance: it helps to predict future sales and stock prices so that people invest their money wisely.
• Healthcare: It helps usto give a better treatment to the patients based on their medical history.

Reinforcement Learning algorithms

There are multiple reinforcement  learning algorithms used in machine learning some of them are:

• Q-learning
• Deep Q-network (DQN)
• Markov Decision Process (MDP)
• Advantage Actor-Critic (A3C)
• State-Action-Reward-State-Action(SARSA)

Choosing the Right Algorithm

To choose the right algorithm among these depends on the data set given.

• Supervised learning: If your data set contains  a labeled dataset and make some prediction based on the given dataset, then supervised learning is used. For example: Image Classification, text classification etc.
• Unsupervised learning:  If your data contains unlabeled dataset and machine should give some training, then unsupervised learning is used, for example: customer segmentation, image segmentation, and recommendation engines.
• Reinforcement learning: If your machine interacts with environment, and learns by a trail-and-error method,reinforcement learning is used.

Conclusion

In Conclusion, supervised learning is used when a model learns from a labeled dataset with guidance. And, unsupervised learning is used where the machine is given training based on unlabeled data without any guidance. Whereas reinforcement learning is used, when a machine or an agent interacts with its environment, performs actions, and learns by a trial-and-error method. So far in this article, we have learned the differences between supervised, unsupervised, and reinforcement learning in detail.