Supervised and unsupervised Machine Learning

Supervised and unsupervised are two main types of tasks in the machine learning field. These two tasks are used in different situations in various types of datasets. The major difference between supervised and unsupervised machine learning is that supervised learning is done when we have information about the project’s output.

Hence, supervised learning is used to learn the function of a project or find the relation between input and output. On the other side, unsupervised learning doesn’t work under the labeled outputs (there are no pre-defined or final outputs) as it learns every step to find the output accordingly.

Many people are confused between supervised and unsupervised machine learning. The article explains everything about the differences between supervision and unsupervised machine learning.

What is Supervised Machine Learning?

Supervised learning trains a system by well “labeled” data. A labeled data means that some of the data is tagged with the correct output. It is similar to a person learning things from another person. Supervised learning is used for regression and classification to predict a procedure’s output. Algorithms in supervised learning learn from the labeled training data, which is beneficial for predicting unpredicted data outcomes. It takes time to build, scale and deploy accurate machine learning models successfully. Besides that, supervised learning also needs an expert team of skilled data scientists.

Some popular supervised learning algorithms are k-Nearest Neighbor, Naive Bayes Classifier, Decision Trees, and Neural Networks.

Example: Suppose we have books of different subjects, the supervised learning can identify the books to classify them according to the subject type. For the proper identification of books, we train the machine by providing the data like color, name, size, the language of every book. After appropriate training, we start to test a new set of books, and the trained system identifies everything using algorithms.

Supervised learning offers a way to collect data output from the previous results and optimize the performance criteria. This machine learning is beneficial for solving different types of real-world computation problems.

How Supervised Machine Learning Works?

Supervised machine algorithms are trained to predict the given project’s output. Below are the steps in supervised learning to train any given algorithm.

First, find the training dataset type, then collect the labeled data.

Now, split all of the training datasets between the test dataset, validation dataset, and training dataset. After splitting the data, determining the training dataset’s input features must have appropriate knowledge so your model can correctly predict the output. Next, determine the required algorithm for that model, like a decision tree, support vector machine, etc. After determining the algorithm, execute the algorithm in the training dataset.

In some cases, users need a validation set as a control parameter, a subset of the training dataset. Finally, you can evaluate the model’s accuracy by giving a test set, and if your model correctly predicts the output, then your model is correct.

Let’s see an example to understand how supervised machine learning works. In this example, we have different shapes like squares, circles, triangles, etc. Now we have to train the data like such that:

• If the shape has four sides, then it must be labeled as the square.
• If the shape has three sides, then it must be labeled as the triangle.
• If the shape has no sides, then it must be labeled as the circle.

When we use a new model in the system, the system will differentiate and detect squares, triangles, and circles.

Types of Supervised Learning Algorithms

There are two types of problem in supervised learning, and they are:

Classification

These algorithms are used when a categorical output variable means when a user compares two different things: true-false, pros-cons, etc. Some of the Classification algorithms are support vector machines, spam filtering, decision trees, random forest, and logistic regression.

Regression

These algorithms are used when there is a relation between and input and output variables. Regression is used to predict continuous variables like Market Trends, Weather forecasting, etc. Some of the Regression algorithms are regression trees, linear regression, Bayesian linear regression, non-linear regression, and polynomial regression.

Advantages and Disadvantages of Supervised Learning

Advantages

• Supervised learning offers a way of collecting the data from previous experiences and predicting the outputs.
• It is beneficial for optimizing the performance through the experience.
• Users can use supervised learning for solving different types of real-world computation issues.
• The feedback system offers a great option to verify if it predicts correct output.

Disadvantages

• In supervised learning, training requires high computation time.
• Users require various examples for every class while training a classifier, then classifying big data becomes a complex challenge.
• Users may overtrain the boundary when the training set doesn’t have any example you need in a class.

Applications

• Bioinformatics: Supervised learning is popular in this field as it is used in our day-to-day lives. Biological information such as fingerprints, face detection, iris texture, and more are stored as data in our smartphones and other devices to secure data and level up the system’s security.
• Speech Recognition: The algorithm is trained to learn voice and recognize it later. Many popular voice assistants such as Siri, Alexa, and Google Assistant use supervised learning.
• Spam Detection: This application helps prevent cybercrime; the applications are trained to detect unreal and computer-based messages and E-mails and alert the user if they are spam or fake.
• Object-Recognition for Vision: The algorithm is trained with a huge dataset of the same or similar objects to identify the object later as or when it comes across.

What is Unsupervised Machine Learning?

Unsupervised learning is a technique of machine learning in which a user doesn’t have to supervise a model for the project. Instead of that, users need to allow a model for work and discover the information automatically. Hence, unsupervised learning works to deal with unlabeled data. In simple words, this type of machine learning aims to find patterns and the structure from the given data or input.

Unsupervised learning offers a great way for performing highly complex processing tasks than supervised learning. However, it can be highly unpredictable than other deep learning, natural learning, and reinforcement learning procedures. Unlike supervised learning, unsupervised learning is used for solving association and clustering.

Unsupervised learning is beneficial to find all types of unknown data patterns. There is the fact that you can easily get unlabeled data as compared to labeled data, so unsupervised learning can help to complete the procedure without the labeled data.

For example, we have a model that doesn’t require any data training, or we don’t have appropriate data to predict the output. So we don’t give any supervision but provide the input dataset to allow a model for finding the suitable patterns from the data. The model will use appropriate algorithms for training then divide the project elements as per their differences. In the above example of supervised learning, we have explained the procedure to get the predicted output. However, in unsupervised learning, the model will train the data itself then divide the book in the group as per their features.

How Unsupervised Learning works?

Let’s understand the unsupervised learning by the below example:

We have unlabeled input data that includes different fruits, but it is not categorized, and the output is also not provided. First, we have to interpret the raw data to find all hidden patterns from the given data. Now will apply the appropriate algorithms like decision trees, k-means clustering, etc.

After implementing the appropriate algorithm, algorithms will divide the data object into combinations based on the difference and similarity between the different objects. The process of unsupervised learning is explained as under:

When the system receives unlabeled or raw data in the system, the unsupervised learning starts to perform interpretation. The system tries to understand the information and given data to start the procedure using algorithms in the interpretation. After that, algorithms start to break the data information into parts according to their similarities and differences. Once the system gets the raw data’s details, it then creates the group to set the data accordingly. Finally, it starts the processing and provides the best accurate output data possible from the raw data.

Types of Unsupervised Learning Algorithm

There are two types of problems in unsupervised learning, and they are:

Clustering

It is a method to group objects in clusters as per the differences and similarities between the objects. Cluster analysis works to find the commonalities between different data objects then categorizes them according to the absence and presence of those particular commonalities.

Association

It is a method that is used for finding relationships between various variables in a large database. It also works to determine the item set that is happening together in a particular dataset. Many people believe that association makes the marketing strategy highly effective, like a person who buys X items and tends to purchase Y items. Hence, the association offers a way to find the relationship between X and Y.

Advantages and Disadvantages of Unsupervised Learning

Advantages

• Unsupervised learning is beneficial for finding the data patterns because it is not possible in normal methods.
• It is the best procedure or tool for data scientists because it is beneficial for learning and understanding the raw data.
• Users can add labels after classifying the data, so it is easier for the outputs.
• Unsupervised learning is as same as human intelligence because the model learns everything slowly for calculating the outputs.

Disadvantages

• The model learns everything without having any prior knowledge.
• There is more complexity with more features.
• Unsupervised learning is a bit of a time-consuming procedure.

Applications

• Host Stays: The application uses Unsupervised Learning to connect users worldwide; the user queries his or her requirements. The application learns these patterns and recommends stays and experiences that fall under the same group or cluster.
• Online Shopping: Online websites like Amazon also use unsupervised learning to learn the customer’s purchase and recommend the most frequently bought products together, an example of association rule mining.
• Credit-Card Fraud Detection: Unsupervised Learning algorithms learn about various patterns of the user and their usage of the credit card. If the card is used in parts that do not match the behavior, an alarm is generated, which could be marked fraud, and calls are given to confirm whether they are using the card.

Supervised Versus Unsupervised Machine Learning: Comparison Table

Here is the list of a side-by-side comparison between supervised and unsupervised machine learning:

Conclusion

We hope we succeeded to explain you the difference between supervised and unsupervised learning. We have added all of the essential details on these machine learning techniques. These machine learning techniques are different but essential in their place. In our opinion, unsupervised machine learning is more accurate than supervised learning as it learns everything on its own to provide the best result possible. However, many people recommend supervised machine learning as they have appropriate inputs and predicted outputs.