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Difference between supervised and unsupervised machine learning

Oct 30th, 2023

In today’s digital age, where open-source data is readily available on the internet, the need to leverage this wealth of information for better decision-making has become a primary objective for numerous companies and individuals.


Machine learning models offer a path to automate decision-making with data, but the crucial first step is selecting the right machine learning algorithms.


In this article, we introduce two fundamental machine learning techniques: Supervised and Unsupervised learning, and conduct a comparative analysis. Let’s dive in!

1- Supervised machine learning:

Within this section, our primary emphasis will be on supervised learning. Initially, we will provide a comprehensive explanation of this concept, followed by an exploration of the rationale behind its designation as “supervised.”


What is Supervised learning :


Supervised learning, a prominent branch of machine learning, revolves around the objective of comprehending the correlation between a given input and its corresponding output. In this context, the input data typically comprises a collection of values, often denoted as features or X variables, while the resultant output is commonly referred to as the target or Y variable. 


Data that encompasses both these features and targets is known as labeled data and is conventionally organized in a structured format known as a “dataset.” Supervised learning leverages statistical algorithms to glean insights into the relationship between the features and the target, a process aptly termed “training.” The culmination of this learning journey yields a “model” capable of making predictions on new, unseen data.

2- Unsupervised machine learning:

Unsupervised learning represents another significant category within the realm of machine learning. In this approach, the dataset used for model training lacks the presence of labels. In simpler terms, the training data comprises solely feature vectors without their associated targets. The primary objective of unsupervised machine learning is to discern similarities, interdependencies, and patterns inherent within the provided inputs.


Unlike supervised learning, the model training process in unsupervised learning doesn’t rely on straightforward input-output mappings; instead, it centers around autonomously uncovering the inherent relationships between various data points within the unlabeled dataset.

3- Comparative analysis: Supervised vs Unsupervised learning

In this section, we will conduct a comparative analysis between supervised learning and unsupervised learning, evaluating them along three distinct dimensions: use-case, complexity, data, and evaluation. Furthermore, we will illustrate each approach with practical examples to facilitate a comprehensive understanding.


3.1-  Use case:


Supervised machine learning models are versatile, as they can be effectively employed for either classification or regression tasks. In this section, we will delve into explaining each of these two techniques, accompanied by tangible examples. Our exploration will commence with classification, followed by a discussion on regression.


Within the context of machine learning, classification pertains to scenarios where the model is tasked with predicting a discrete value as the target. When this target can assume one of two distinct labels, such as “Yes” or “No,” “True” or “False,” it falls into the realm of a binary classification problem. In contrast, if the target variable could encompass a range of multiple possible values, such as “green,” “yellow,” or “blue,” we are confronted with a multiclass classification problem.

classification models have a wide array of applications across different domains. Here are some examples:

  • Fraud Detection: Financial institutions employ classification models to identify potentially fraudulent transactions.
  • Medical Diagnosis: Classification models can assist in diagnosing medical conditions, such as identifying diseases based on patient data and test results.
  • Spam or Offensive Content Detection: Leveraging classification models, it becomes possible to distinguish between spam and legitimate emails . Additionally, these models enable the detection of harmful or offensive content within social media posts, contributing to safer online environments.



Regression diverges from classification based on the nature of the target variable. In regression problems, the predicted target is continuous. For instance, it might involve forecasting the temperature as 20 degrees or estimating the price of a particular product.


Regression models find extensive utility in a multitude of domains. Here are some examples:


  • Quality Control: In manufacturing, regression models can  predict product quality based on various input variables.
  • Marketing: Regression is used in marketing to forecast sales, customer lifetime value, and return on investment for marketing campaigns.
  • Housing Price Prediction: In real estate, regression can be used to estimate the selling price of a house based on features like square footage, and number of bedrooms.

An instance of regression algorithms is linear regression, Linear regression operates by seeking the best-fitting linear function, typically depicted as a straight line, to represent the connection between input features and the output variable. This acquired linear function enables the prediction of ‘Y’ for new sets of features, making it a valuable tool for numerical predictions across a broad spectrum of applications.

supervised machine learning

When it comes to unsupervised learning Clustering and dimensionality reduction are two prominent use cases. 





Clustering is based on the idea that instances belonging to the same group tend to share similar features with other instances from the same group. The goal of clustering algorithms is to group data points with shared characteristics into “clusters”.



Here are some examples of clustering applications:



  • Customer Segmentation: Businesses use clustering to group customers with similar purchasing behavior, helping in targeted marketing and product recommendations.

  • Spatial Data Analysis: Clustering is applied to analyze geographic data, such as identifying areas with similar population densities, land use patterns, or disease prevalence.

  • Natural Language Processing (NLP): In NLP, clustering is used for text document clustering, grouping similar documents together, and topic modeling for identifying themes in a corpus of text.

3.2- Data:


A significant differentiation between supervised and unsupervised learning lies in the type of dataset employed for training.

Supervised learning algorithms make use of annotated datasets; this annotation process typically necessitates human intervention, either for the initial labeling or to assess the quality of the labels.

machine learning

Conversely, unsupervised learning operates with unlabeled datasets, eliminating the need for human intervention in the labeling process. 

supervised machine learning

3.3- Evaluation:


Assessing the performance of a supervised learning model typically entails the utilization of performance metrics such as precision, accuracy, recall, and the F1 score. These metrics serve as indicators of the model’s ability to generalize to previously unseen data, essentially gauging its alignment with the dataset.


Unsupervised learning presents a more intricate evaluation scenario due to the absence of a ground truth, or labeled data, for benchmarking the model’s predictions.


Consequently, the evaluation process often revolves around the utilization of metrics that are tailored to the specific characteristics and requirements of the problem domain at hand.


In summary, the realm of machine learning is in a constant state of progression. Supervised learning enhances the capabilities of statistical algorithms, enabling them to provide precise forecasts and categorizations when furnished with labeled data.


Conversely, unsupervised learning introduces a crucial facet to machine learning by uncovering concealed patterns, structures, and irregularities within unorganized data.