Supervised vs. Unsupervised Learning: Knowing the Difference
My Personal Learning Experience
I recently started taking courses related to tech, didn’t think that would ever happen but here we are! I got to do this course titled; “AI for Everyone” on Coursera, and one of the lessons focused on supervised and unsupervised learning. Fascinated about the subject, and since it is safe to call me a budding technical writer, I decided to make further research on the distinctiveness that exists between both. Today, I would be sharing my findings in the most basic way you don’t have to be a tech guru to relate.
Welcome to my tech class for dummies, lol
What is Supervised and Unsupervised Learning?
Generally speaking, supervised learning can be applied when you need to classify data or make predictions while unsupervised learning entails understanding the relationships that exist within a dataset. These terms are two essential models as far as machine learning is concerned, they both have their unique characteristics and use cases. I would first outline some of the differences there are between supervised and unsupervised learning;
Supervised Learning
It varies from unsupervised learning in objective, data labeling, examples, algorithm types, and evaluation.
Objective
The objective of supervised learning is to grasp a mapping function that has the ability to classify new or unseen data and make predictions. This is achieved by the algorithm learning to chart input data from its source to an output variable.
Data Labeling
In supervised learning, the dataset must be labeled. This way, each input corresponds with a corresponding label. This labeled data is what the model learns from and then can make predictions when new or unlabeled data is made available.
Examples
The examples of application of supervised learning includes; analysis of sentiments in texts, classification of images, regression tasks that involves predicting the prices of houses, and speech recognition.
Algorithm Types
The algorithm types applied for supervised learning includes; decision trees, linear regression, neural networks, and support vector machines.
Evaluation
The models in supervised learning are evaluated using these metrics like accuracy, mean squared error, and precision among others depending on the task that needs to be evaluated.
Unsupervised Learning
It varies from supervised learning in objective, data labeling, examples, algorithm types, and evaluation.
Objective
The goal of unsupervised learning is to group similar data points that exist within datasets. This is done by discovering structures, hidden patterns, or relationships in data without using a set of labeled target variables.
Data Labeling
In unsupervised learning, unlabeled data are employed to carry out analysis. What this means is that the inherent structure or cluster in the dataset is explored by the algorithm.
Examples
The examples of application of unsupervised learning include anomaly detection, dimensionality reduction and generative modeling.
Algorithm Types
The algorithms popular with unsupervised learning includes generative adversarial networks (GANs), hierarchical clustering, k-means clustering, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE).
Evaluation
The evaluation of unsupervised learning models could pose a challenge because the target labels are not clear. ML Engineers rely on a course of action like visual inspection of results among others.
Key Differences
The major differences that exist between these two can be noted in data labeling, objective, examples and evaluation.
Data Labeling
You work with labeled data for supervised learning while you work with unlabeled data for unsupervised learning.
Objective
In supervised learning, the goal is to classify data and make predictions based on the data classified but in unsupervised learning, you aim to uncover hidden patterns existing in datasets and group data points that are similar.
Examples
Supervised learning can be applied for tasks like classification and regression but unsupervised learning is applied to cluster, reduce dimensionality, and generative modeling.
Evaluation
The evaluation of supervised learning models employs clear metrics that relate to prediction accuracy while the evaluation of unsupervised learning relies on measures that are not often straightforward.
Conclusion
Summarily, the choice to apply supervised or unsupervised learning is determined by the nature of the complication, the type of data that is available, and the goals of the task. I hope you were able to learn a thing or two from this exposition to supervised and unsupervised learning, to learn more from Reispar Technologies, follow our medium page, you can send an email to academy@reispartechnologies.com.ng to learn more about our courses on Business intelligence and Data/AI. Do you want to talk to an AI expert? Schedule a meeting.
Written by:
Adeoye Esther Ifeoluwa
