Supervised vs. unsupervised learning describes two main types of tasks within the field of machine learning. In supervised learning, the researcher teaches the algorithm the conclusions or predictions it should make. In Unsupervised Learning, the model has algorithms able to discover and then present inferences about data. There is no teacher or single correct answer. Thus the machine learns on itself.

Aspect	Supervised Learning	Unsupervised Learning
Definition	– Supervised Learning is a type of machine learning where the algorithm learns from labeled training data, making predictions or decisions without human intervention. It’s supervised because it involves a “teacher” who provides the algorithm with the correct answers during training.	– Unsupervised Learning is a type of machine learning where the algorithm learns patterns and relationships from unlabeled data. It explores the data’s inherent structure without specific guidance on what to look for.
Training Data	– In Supervised Learning, the training dataset consists of input-output pairs, where each input is associated with the correct output or label. The algorithm learns to map inputs to corresponding outputs.	– In Unsupervised Learning, the training dataset contains only input data, without any associated labels or outputs. The algorithm identifies patterns, groupings, or structure within the data.
Goal	– The primary goal of Supervised Learning is to learn a mapping function from inputs to outputs that can generalize to make accurate predictions on new, unseen data. It’s used for tasks like classification and regression.	– The primary goal of Unsupervised Learning is to discover the underlying structure, relationships, or patterns within the data. It’s used for tasks like clustering and dimensionality reduction.
Examples	– Supervised Learning examples include email classification (spam or not spam), image classification (identifying objects), and predicting house prices based on features.	– Unsupervised Learning examples include clustering customer data to identify market segments, reducing the dimensions of a dataset, and anomaly detection in network traffic.
Algorithms	– Supervised Learning algorithms include decision trees, random forests, support vector machines, and deep neural networks.	– Unsupervised Learning algorithms include k-means clustering, hierarchical clustering, principal component analysis (PCA), and autoencoders.
Evaluation	– In Supervised Learning, model performance is typically evaluated using metrics like accuracy, precision, recall, F1-score (for classification), or Mean Squared Error (for regression).	– In Unsupervised Learning, evaluation can be more challenging since there are no predefined correct answers. Common evaluation metrics include silhouette score (for clustering) or explained variance (for dimensionality reduction).
Use Cases	– Supervised Learning is well-suited for tasks where the desired outcome is known, and labeled data is available for training. It’s used in applications like image recognition, natural language processing, and recommendation systems.	– Unsupervised Learning is applied when you want to explore data, find hidden patterns, group similar data points, or reduce data dimensionality. It’s used in fields like data compression, anomaly detection, and customer segmentation.
Data Preparation	– Data for Supervised Learning requires labeled examples, which often involve manual labeling or annotation. Clean, structured datasets with well-defined features are essential.	– Unsupervised Learning can work with raw, unlabeled data, making it useful when labeled data is scarce or expensive to obtain. Data preprocessing might involve scaling and dimensionality reduction.
Challenges	– Supervised Learning relies heavily on the quality and representativeness of labeled training data. Biased or insufficient data can lead to biased models.	– Unsupervised Learning may struggle with finding meaningful patterns in noisy or high-dimensional data. The interpretation of results can also be challenging.
Applications	– Supervised Learning applications include image recognition (e.g., classifying objects in photos), sentiment analysis (determining sentiment in text), and predicting customer churn in a business.	– Unsupervised Learning applications include clustering news articles by topic, reducing the dimensions of genomic data for analysis, and identifying network intrusions by detecting anomalies.
Semi-Supervised Learning	– Semi-Supervised Learning is a hybrid approach that combines elements of both Supervised and Unsupervised Learning. It uses a small amount of labeled data along with a larger unlabeled dataset to train models.	– Unsupervised Learning does not inherently incorporate labeled data, as its primary focus is on discovering patterns in unlabeled data. However, semi-supervised techniques can be applied to combine labeled and unlabeled data for specific tasks.
Interpretability	– In Supervised Learning, models are often more interpretable because they learn explicit mappings from inputs to outputs. Decision trees, for example, provide clear decision paths.	– Unsupervised Learning models may be less interpretable since they focus on data structure rather than explicit mapping. Clustering results might not have straightforward interpretations.
Data Labeling Cost	– Supervised Learning can be costly because it requires labeling data, which can be time-consuming and expensive, especially for large datasets.	– Unsupervised Learning can be more cost-effective since it doesn’t require labeled data, making it suitable for scenarios with limited resources for data annotation.
Overfitting Concerns	– Supervised Learning models can be prone to overfitting if they memorize the training data rather than generalizing from it. Techniques like regularization are used to mitigate this risk.	– Unsupervised Learning models can also overfit in certain scenarios, but the risk is often lower since there are no target labels to memorize. Careful preprocessing and model selection can help.
Hybrid Approaches	– Some tasks benefit from combining both Supervised and Unsupervised Learning. For example, pretraining a deep neural network with unsupervised data and fine-tuning with labeled data (a form of transfer learning) is common in deep learning.	– Hybrid approaches can leverage the strengths of both Supervised and Unsupervised Learning to address complex problems, such as anomaly detection with limited labeled examples.

Table of Contents

Supervised vs. unsupervised learning

What is supervised learning?

Supervised learning involves training a machine with well-labeled data. In other words, some input data is already tagged with the correct answer.

What is unsupervised learning?

Unsupervised learning, on the other hand, involves training a machine with data that is neither labeled nor classified. In this case, the algorithm acts on information and draws conclusions without human guidance.

Choosing between the supervised and unsupervised approach

Machine learning algorithms are trained according to the data available and the research question at hand. But, in any case, researchers who fail to identify the objective of the machine learning algorithm will not be able to build an accurate model.

In essence, the ability to build an accurate model comes down to a matter of choice. Algorithms can be trained using one of two models that help them make predictions about data:

Supervised learning – where the researcher teaches the algorithm the conclusions or predictions it should make.
Unsupervised learning – where the algorithm is left to its own devices to discover and then present inferences about data. There is no teacher or indeed single correct answer.

The next sections will look at each model in detail.

Supervised learning

In supervised learning, the researcher teaches the algorithm to use data that is well-labeled. That is, some of the data is already tagged with the correct answer. Then, the algorithm is provided with a new set of examples called training data that it uses to produce a correct outcome based on the labeled data.

Supervised learning problems can be categorized as:

Classification problems – where the output variable is a category such as “green” and “yellow” or “yes” and “no”. Examples include spam detection, face detection analysis, and the automated marking of exams.
Regression problems – where the output variable is a real value, such as “dollars” or “kilograms”. Regression algorithms (linear regression models) are used in any scenario requiring a prediction of numerical values based on previous observations. Examples include house and stock price predictions and weather forecasting.

Supervised learning algorithms

Note that multiple algorithms and computation techniques are used in a supervised learning process. A brief description of some of the more common techniques is provided below.

Neural networks

To process training data, neural networks mimic the interconnectivity of a human brain with various layers of nodes. Each of these nodes is comprised of inputs, weights, a threshold, and an output. When the output value exceeds the threshold, it activates the node and passes data to the next layer in the network.

Neural networks are primarily used in deep learning algorithms that solve some of the regression problems mentioned above.

K-nearest neighbor

The K-nearest neighbor (KNN) algorithm classifies data points according to their association and proximity to the other available data. The KNN algorithm thus assumes that the most similar data points are those found in close proximity. It first calculates the distance between data points and then attributes a category based on its frequency or average.

KNN is the preferred supervised learning algorithm for data scientists because it is simple to use and offers a low calculation time. However, as the size of the dataset increases, so too does the processing time. This makes it less suited to classification tasks and more suited to use in image recognition and recommendation engines.

Linear and logistic regression

Linear regression makes predictions about future outcomes by determining the relationship between one dependent variable and one or multiple independent variables.

Logistic regression is chosen when the dependent variables are categorical. This means they tend to be best suited to classification problems with binary outputs such as spam identification.

Random forest

A random forest is constructed from decision tree algorithms and can be used for both regression and classification problems. Decision trees form the basis of the random forest and consist of three components: leaf nodes, decision nodes, and a root node. Nodes represent the attributes that are used to predict outcomes.

The tree divides the dataset into branches which further divide into other branches and so on. The process continues until a leaf node is attained which cannot be divided further. Some of the major applications include:

Banking – to determine the creditworthiness of a loan applicant.
Healthcare – to diagnose patients based on their medical history, and
eCommerce – to predict consumer preferences based on past consumption behavior.

Unsupervised learning

Unsupervised learning involves training an algorithm with information that is neither labeled nor classified. Instead, the algorithm must group unsorted information according to patterns or similarities in the data without prior training.

Unsupervised learning algorithms are commonly used in:

Clustering tasks – where the goal is to discover inherent groupings in the data. For example, a marketing agency may use an algorithm to segment a target audience by purchasing behavior.
Dimensionality tasks – where the algorithm seeks to reduce the number of variables, characteristics, or features in a dataset. Since some of these dimensions are correlated, redundant or repeated information can increase dataset noise and impact the training and performance of the model. This technique is often used in the data preprocessing stage such as when noise is removed from visual data to improve picture quality.

Association tasks – where the algorithm must find association rules in the data. The same marketing agency may look at what consumers tend to buy or do after purchasing a certain product. These tasks also form the basis of recommendation engines that show “Customers who purchased this product also bought” messages.
Anomaly tasks – where the algorithm searches the data for rare items or events. Many financial institutions use anomaly algorithms to detect instances of fraud in bank account records. Antivirus software also uses similar technology to identify malware.

Other unsupervised learning tasks

While clustering, dimensionality, association, and anomaly tasks are some of the most frequent an unsupervised learning algorithm will encounter, there do exist other types.

Density estimation

Density estimation, which has its roots in statistical analysis, estimates the density of the distribution of data points. In machine learning, density estimation is used in conjunction with anomaly tasks since data points in low-density regions tend to be outliers.

The distribution of data points is formally known as the probability density function (PDF). This can be used to determine whether the occurrence of a specific outlier is unlikely or whether its occurrence is so unlikely that it should be removed from the dataset.

Association rule learning

Association rule learning is another unsupervised learning task primarily used by businesses to maximize profits. It analyzes datasets to discover relationships between variables that are non-obvious and requires a complex algorithm such as Apriori, FP-Growth, or Eclat.

One application of association rule learning is product placement. Consider a supermarket that analyzes a transaction dataset to discover that consumers often buy bread with milk and onions with potatoes.

Based on the relationships the algorithm detects, the supermarket can then place the items near each other to maximize revenue and profits. Insights from these relationships can also be used in promotional pricing and marketing campaigns.

Choosing between supervised and unsupervised learning

Machine learning is a vast field and as a result, choosing the right machine learning process can be difficult and resource intensive.

In very general terms, however, it is important to assess these pointers:

Evaluate data. Perhaps an obvious point, but one worth mentioning. Is it labeled or unlabeled? Could expert consultation facilitate additional labeling?
Define the goal. Is the problem defined and likely to reoccur? Alternatively, will an algorithm have a better chance of identifying unknown problems ahead of time?
Review the available algorithms. Which are best suited to the problem in terms of the number of features, attributes, or characteristics? Algorithm choice should also be sensitive to the overall structure and volume of data to be analyzed.
Study historical applications. Where has the algorithm already been used to great success? Consider reaching out to organizations or individuals who have demonstrable skills in a comparable field.

Summary of the core differences

To conclude this comparison on supervised and unsupervised learning, let’s discuss the core differences according to an assortment of parameters.

Input data – supervised learning algorithms are trained on labeled data, while unsupervised algorithms are not.
Computational complexity – supervised learning is a simpler method that only requires a program like Python or R. Unsupervised approach is more complex and thus requires more powerful tools.
Accuracy and classes – supervised learning is more accurate, trustworthy, and the number of classes is known. The number of classes is not known in unsupervised learning, and it tends to be less accurate and trustworthy.
Data analysis – while supervised learning analyzes offline data, the unsupervised approach analyzes data in real time.
Objective – the objective of supervised learning is to predict outcomes for new data. The objective of unsupervised learning is to gather data insights based on what the model determines is interesting or different.
Potential drawbacks – supervised learning is a training approach that takes time and human expertise. On the other hand, unsupervised learning can yield inaccurate or worthless results unless there is a human to validate output variables.

Key takeaways

Supervised learning involves training a machine with data that is well-labeled. In other words, some input data is already tagged with the correct answer. Unsupervised learning involves training a machine with data that is neither labeled nor classified.
In supervised learning, the researcher teaches the algorithm to arrive at a desirable answer given labeled data points. It has applications in examination marking, facial recognition, and weather forecasting.
In unsupervised learning, the algorithm must group unsorted information that is neither labeled nor classified without instruction. Unsupervised learning has important uses in detecting bank fraud and malware. It is also used to identify patterns in consumer buying behavior.