Difference Between Supervised and Unsupervised Learning

Introduction

Technologies like machine learning, artificial intelligence, and data analytics thrive on data to automate complex tasks. The use of data is not restricted to only processing and interpretation to stay ahead of competitors, provide better customer services, and build effective business strategies, but also to train, test, and evaluate the models. In machine learning, data is classified into three categories, training data, validation data, and testing data. As the name suggests, training data trains a model or an algorithm in machine learning. The model learns from input and output training data sets and predicts classification or performs specific tasks. We use training data for both supervised and unsupervised learnings of an algorithm. 

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This blog discusses these two broad categories of machine learning – supervised and unsupervised learning and their differences in detail.

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What is Supervised Learning?

Supervised learning, a subset of machine learning and artificial intelligence, is an algorithm teaching technique that uses labeled data to train algorithms. It teaches algorithms how to perform tasks like classification and regression in datasets. In supervised learning, the algorithm receives input-output training samples and uses these samples to establish a relationship between datasets. Since we provide labeled training data to the algorithm to perform tasks under supervision, we term it supervised learning. The main objective of supervised learning is to feed data to the algorithm to understand the relationship between the input and the output. Once the algorithm establishes a connection between the input and output, it can accurately deliver fresh results from newer inputs.

Let us understand how supervised learning works. Suppose in a machine learning algorithm we have an input X and output Y. We feed or provide input X to a learning system in a model. This learning system will deliver an output Y’. An arbitrator in the system checks the difference between Y and Y’ and produces an error signal. This signal passes on to the learning system that understands the difference between Y and Y’ and adjusts the parameters to reduce the difference between Y and Y’. Here, Y is the labeled data.

The supervised learning process involves multiple steps. 

• Initially, we must determine the training dataset type and then collect the labeled training data. We also need to arrange data differently for classification or regression.
• The next step is to use an algorithm for supervised learning like a support vector machine or decision tree and then determine the input features for the learning model.
• Now, execute the learning process and adjust or control parameters.
• The last step involves testing the accuracy of the model. 

The entire supervised learning process trains the learning system to adjust parameters, so the algorithm provides a minimum output difference. Supervised learning facilitates two complex processes in data mining – classification and regression. In classification, the data is categorized or labeled in different classes based on similar attributes like spam filters. We use regression to predict continuous observations, for instance, the stock market or the heart rate. Regression gives real number values.

The following are the different types of supervised learning algorithms:

• Naive Bayes:- The Naive Bayes Classifier is based on the Bayes theorem. This algorithm assumes that all the features of a class are independent of each other. The Naive Bayes classifier uses the conditional probability method to predict classification.
• Support Vector Machine:- It is a popular machine learning algorithm for classification and regression tasks.
• Linear Regression:- The linear regression algorithm uses supervised learning to predict future outcomes. It establishes a relationship between one dependent variable and two or more independent variables.
• Logistic Regression:- We use a logistic regression algorithm when we have variables in different categories like yes or no, and true or false. We mainly use a logistic regression algorithm to solve binary classification problems.

To sum up, supervised learning is used to train a model using known input and output data to generate predictions for a new set of inputs.

What is Unsupervised Learning?

Unlike supervised learning, we do not have labeled data in unsupervised learning. There is no predefined relationship between datasets or a predicted outcome. Contrary to supervised learning, unsupervised learning requires minimum human intervention. Hence, we call it unsupervised learning. The model uses a collection of dataset observations and describes the properties of given data. Unsupervised learning is based on a clustering framework because it identifies various groups in a dataset.

Let us understand how unsupervised learning works. Suppose we have a series of inputs named X1, X2, X3…….Xt but no target outputs. In this case, the machine does not get any feedback from its environment. However, it develops a formal framework and predicts future outputs. In unsupervised learning, the model uses inputs for decision-making and building representations. We cannot use unsupervised learning for classification and regression processes due to the absence of output data. The primary use of unsupervised learning is to figure out the underlying structure of the input dataset. Machine arranges data in different groups based on the interpretation after finding the structure. The last step is to represent the dataset in a compressed format.

Engineers mostly use unsupervised learning for two purposes – Exploratory analysis and dimensionality reduction. Exploratory analysis performs initial investigations on data to arrange it in different groups, build hypotheses, and discover patterns. The dimensionality reduction process reduces the number of inputs in a given dataset. The most significant advantage of unsupervised learning includes finding relevant insights. Unsupervised learning is mainly used to build AI applications because it requires minimum human intervention.

Supervised vs. Unsupervised Learning

Now that you know what supervised and unsupervised learnings are, let us look at their most significant differences.

• Data – Supervised learning uses labeled data, whereas unsupervised learning does not use labeled data. Also, we provide output data to the model in supervised learning. However, in unsupervised learning, input data is not available.
• Feedback-  The model takes feedback and adjusts parameters in supervised learning. It does not happen in unsupervised learning.
• Goal – The primary objective of supervised learning is to train the model using training data. So, when a new input is available, the machine can predict the accurate output. However, since the output is not available in unsupervised learning, it is used to gather relevant insights or hidden patterns in given data.
• Classification and Regression – We can categorize supervised learning into classification and regression, which does not happen in unsupervised learning.
• Artificial Intelligence – Supervised learning is not relevant for artificial intelligence because we have to feed training data into the model. However, unsupervised learning is more beneficial for artificial intelligence because it requires minimum human intervention.
• Algorithms – Supervised learning algorithms include Support Vector Machine, Naive Bayes, linear regression, and logistic regression. Unsupervised learning algorithms include clustering and K-nearest neighbor (KNN).
• Accuracy of results – As the model gets predetermined output in supervised learning, it gives more accurate results. However, the results of unsupervised learning are subjective and give less accurate results.

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Conclusion

Supervised and unsupervised learning are the basic concepts of machine learning, setting the foundation for learning complex concepts. If you have a keen interest in machine learning and want to build a career in the same, you can pursue a Master of Science in Machine Learning & AI from upGrad. 

Industry leaders teach this course to help you gain in-depth theoretical knowledge of machine learning and practical insights into machine learning technology. Moreover, you get opportunities to work on several case studies and projects on machine learning to help you acquire relevant skills.