Supervised vs. Unsupervised Machine Learning – What’s the Difference?
Supervised and unsupervised learning are two main approaches to building machine learning (ML) models. They have fundamentally different approaches to training, as well as different use cases. This article explains both methods and the differences between them.
Supervised Machine Learning
This approach involves training an ML algorithm on labeled data sets. For each example in the training set, the algorithm knows which output is correct. It uses this knowledge to try to generalize to new examples it has never seen before. By applying this information, the model can gradually learn and improve its accuracy. In labeled data, each “input” is associated with a correct “output.” For example, in a set of medical images, each would be associated with an indication of whether the input image contains features of the disease in question.
The goal of this method is to enable the model to establish a relationship between output and input data. It trains by iteratively making predictions on inputs and adjusting its parameters to obtain the correct answer. Supervised learning is most commonly used in medical diagnostics, spam and fraud detection, speech recognition, customer churn prediction, product recommendations, and sentiment analysis.
Supervised ML consists of two subcategories: classification and regression.
In classification, the model predicts the correct label of the input data. It is fully trained and tested. It can then be used to make predictions on new, unknown data. Examples of classification using supervised machine learning in everyday life:
Healthcare. Training a model on historical patient data can help medical professionals accurately analyze diagnoses. During the COVID-19 pandemic, models have been implemented to effectively predict whether a person has COVID-19 or not.
EducationUnstructured information from text, video and audio data can be analyzed using natural language processing models to perform tasks such as classifying documents into categories, automatically detecting the language of student application documents, and analyzing student reviews.
TransportThis industry uses machine and deep learning models to predict increased traffic in a given geographic area, potential traffic problems due to weather conditions, etc.
Sustainable agricultureUsing classification models, it is possible to predict which type of soil is best suited for a particular type of seed.
Another subcategory of supervised ML is regression, which produces a continuous value. For example, probability or price. There are linear and logistic regressions.
Linear regression. A simple algorithm that models a linear relationship between one or more explanatory variables and a continuous numeric output variable. It is faster to learn than other machine learning algorithms. Its biggest advantage is its ability to explain and interpret the model's predictions. It is used to forecast sales or to predict continuous values, such as house prices, for example.
Logistic regression. Does not perform statistical classification, but estimates the parameters of a logistic model. The reason it can be used for classification is because of the decision boundary that is inserted to separate the classes. Thus, in its simplest form, logistic regression uses a logistic function to model binary dependent variables.
Other common types of algorithms for performing classification and/or regression are:
Decision trees. It is a non-parametric algorithm that can also perform regression and classification. It is clear and easy to visualize and interpret. Conceptually, a decision tree can be thought of as a flow from the root to the leaves. The path to the leaf from the root determines the decision rule adopted based on the features.
Random forests. It is an algorithm that uses bootstrap aggregation and random subspace to grow individual trees to produce a powerful aggregate predictor capable of both classification and regression. The goal is to reduce the correlation between predictors for the aggregate model.
Support vector machines. They are used for image classification and text categorization.
Neural networks. They study complex patterns in data and are used for image and speech recognition.
Benefits of supervised learning:
high forecasting accuracy;
wide range of applications;
high degree of interpretability;
control of the learning process;
evaluation of algorithm performance;
incremental learning.
Flaws:
data availability problem;
dependence of the reliability of forecasts and the effectiveness of the model on the quality and consistency of the markup;
dependence of the model's performance on correctly selected input variables;
difficulty of scaling;
limited patterns, since they are only within the provided training data sets;
high computational cost.
Unsupervised Machine Learning
It is an approach used to discover the underlying structure of data. Unsupervised learning algorithms do not require mapping of inputs and outputs and therefore human intervention. They are typically used to discover existing patterns in data so that instances are grouped together without the need for labels. It is assumed that instances that fall into the same group have similar characteristics.
This method is most commonly used in scenarios such as customer segmentation, anomaly detection, market basket analysis, document clustering, social network analysis, image compression.
Unsupervised machine learning models group data and are used to solve three main problems:
Clustering. Grouping similar data points into clusters. Used for customer segmentation, where companies can group customers based on similarities (such as age, location, or purchasing habits).
Association. Finding relationships between variables. Association rules are often used in market basket analytics.
Dimensionality reduction. The algorithm quantitatively reduces the variables in the data, but tries to preserve as much information as possible. This method is used during preliminary work with the data. Example: improving image quality by removing noise using an autoencoder.
The most common unsupervised machine learning algorithms and methods are:
K-means clustering. A popular and widely used algorithm that partitions data into so-called k-clusters. Each data point is assigned to the nearest cluster center, and cluster centers are recalculated iteratively. Often used for document clustering, image compression, and market segmentation.
Hierarchical clustering. The algorithm builds a hierarchy of clusters in two ways: agglomerative (bottom-up approach) and divisive (top-down approach). It is used for organizing documents and analyzing social networks.
DBSCAN. The algorithm groups data points that are densely packed and marks those that lie apart as outliers. DBSCAN assumes that clusters are dense regions in space, separated by regions of lesser density. Unlike k-means, DBSCAN infers the number of clusters from the data and can detect arbitrarily shaped clusters. It is used for spatial data analysis and noise filtering.
Principal Component Analysis. Transforms data into a set of uncorrelated components that maximize variance. This process reduces the dimensionality of the data. The method is used in gene expression analysis, image compression, and exploratory data analysis.
Forests of Isolation. The algorithm creates a set of trees by randomly selecting a feature and splitting the data. The algorithm then detects anomalies by looking for points that require fewer splits to isolate. The method is used for network security and fraud detection.
One-class SVM. This method learns the boundary that separates normal data points from outliers. It is used for high-dimensional data and in anomaly detection problems such as detecting manufacturing defects or credit card fraud.
Advantages:
no need for labeled datasets;
hidden patterns are revealed;
dimensions are reduced;
anomalies and outliers in the presented data are identified;
economic efficiency increases.
Flaws:
difficulties in interpreting results due to lack of labels;
there are no clear metrics;
resource intensity;
problems of retraining;
dependence on the quality of the features used.
Supervised vs. Unsupervised Machine Learning: A Comparison
Supervised machine learning involves using training sets. For example, an algorithm can predict how long a driver will be on the road given the time of day, weather, etc. But first, the model would have to be taught to understand what rainy weather is and how it increases driving time.
Unsupervised machine learning models work on their own and discover the internal structure of unlabeled data. These models do not require human intervention. They do not make predictions, but only automatically group data. For example, images by the objects they contain (people, animals, buildings, etc.), without knowing in advance what these objects are. If you use an unsupervised learning model on the same dataset of car commutes, it will group trips with similar conditions, such as time of day and weather, but will not be able to predict the travel time.
How to choose correctly
Supervised ML is used more often than unsupervised ML because it is more accurate and efficient. In turn, unsupervised ML can be used for data that is not labeled, which is common. It can also be used to find hidden patterns in data that supervised learning models cannot detect. Supervised learning is problematic for classifying big data, but the results obtained will be extremely accurate. Unsupervised learning algorithms are easier to process big data in real time, but the final results are less accurate.
But it’s not an either/or choice. There’s a middle ground known as semi-supervised learning. This uses a training dataset with both labeled and unlabeled data. This is useful when it’s hard to extract relevant features from large datasets. For example, such an algorithm could be used on a dataset with millions of images, of which only a few thousand are labeled.
Semi-supervised learning is best suited for medical imaging, where a small amount of training data can lead to significant improvements in the accuracy of results. For example, a radiologist might label a small subset of CT scans for tumors or other abnormalities, and the machine could more accurately predict which patients might need additional medical attention. This would not require labeling the entire dataset.
