Supervised Learning vs. Unsupervised Learning
Introduction to Supervised and Unsupervised Learning
It’s true that technologies are evolving continuously and due to its rapid evolution many technologies become obsolete and many newer technologies come into the forefront.
Machine Learning (ML) and Artificial Intelligence (AI) have become the technologies that every company wants to leverage nowadays, as it is believed to lead some of the most disruptive technological changes of our times. Even though there are fundamental differences between AI and ML, these are, in most cases, considered under one head.
In this article, we are going to take a look at the two methods in machine learning and their key differences – supervised and unsupervised Learning. But first things first, let’s get an understanding of these two types and what they do.
In simple terms, supervise means to observe and oversee the execution of a particular task to see that is completed correctly. Likewise, supervised learning in machine learning means supervising a machine learning model. Basically, you load and train the machine with inputs which are already tagged with the correct output or as we call it “labeled data.” It means that users have prior knowledge of precisely what our outputs are going to be, and then with the use of an algorithm, the corresponding outputs of a new unforeseen data set could be predicted.
Similar to a supervisor teaching and correcting a learning student, the learning algorithm also makes predictions on the given data set based on previous learning, and the supervisor adjusts it until the algorithm achieves its expected accuracy. Supervised learning empowers us to solve real-world computation problems, performance optimization, etc.
Unsupervised learning, on the other hand, is where the machine is given only the input data but not the corresponding output data or “unlabeled data”. It allows the device to work on its own to come up with an output just like where a student is given a data set without samples of correct outputs to work on his own without any correction and supervision. This could be more unpredictable than supervised learning, but it could be used to solve more complex problems as the algorithm tries to find all kinds of possible data patterns and solutions in real-time. Also, it is much easier to load the machine with unlabeled data as more manual intervention is required for data labeling.
Supervised vs unsupervised learning
Now let’s take a look at a feature by feature comparison between the two types, starting with the simpler differences.
1. Input data
In supervised machine learning, algorithms are trained and supervised using the known and labeled data, whereas the unsupervised technique uses the unlabeled data. Less manual intervention is required in the latter.
The machine is given the input and corresponding output variables beforehand usually by data scientists in the supervised model whereas only input data is given to the machine to work out a solution or discover patterns on its own in the unsupervised model.
3. Use of data
Supervised machine learning maps input and output data samples to get trained and learn a link in data mapping. Unsupervised machine learning uses no output data.
The expectancy of supervised machine learning is to establish a connection between the given input and output data sets and determine the outcome accurately when a new data set is provided. The objective of the unsupervised algorithms is to identify the underlying structure or identify all possible data patterns of the given input variables to learn about the datasets.
The accuracy of the results of the supervised model is very high and reliable compared to the less trustworthy and moderately accurate unsupervised model.
Supervised machine learning takes place offline, whereas unsupervised learning takes place in real-time.
The computational complexity of the supervised method is simpler compared to the complexity of the unsupervised method.
8. Algorithms used
Some of the most popular algorithms used in supervised machine learning can be named as SVM (Support Vector Machine), classification trees, random forests, linear regression, and logistic regression. Some of the popular algorithms used in unsupervised learning can be given as K-means algorithms and clustering algorithms.
Supervised algorithms are often used for speech and image recognition, forecasting, predictions, and financial analysis. Unsupervised algorithms are usually used for preprocessing of data or exploratory data analysis to summarize data according to their characteristics.
There are two main types of problems where supervised learning could be used; classification and regression problems.
When the output data is a category, the algorithm determines the input data as a member of a certain category or a group. For example, take a training data set of vehicle images where the image is pre-labeled as car, van, and bike. The supervised algorithm is assessed by how accurately it can classify new images of vehicles into the given categories. Support Vector Machine is a popular algorithm used in classification scenarios.
When the output data is a real value such as height or width, the algorithm looks at continuous data and predicts the continuing value. In popular cases, the expected value is used to identify the relationship between the given values of the dataset. For example, calculating the value of the ‘C’ variable if the values of ‘a’ and ‘b’ variables are given. Some popular regression algorithms are polynomial regression, logistic regression, linear regression, etc.
The use of unsupervised algorithms could also be categorized according to the types of clustering, association, anomaly detection, and autoencoders.
This allows the algorithm to process the unstructured data and find a pattern and then cluster (group) those data according to the data patterns or characteristics that exist in the given data. Users can also determine how many clusters the algorithm should identify. For example, given a set of dog images; the algorithm will roughly cluster them into groups according to common features like height, shape, fur coat, tail, etc. Some popular clustering algorithms are connectivity models, K-means, hierarchical clustering, etc.
This unsupervised technique deals with large datasets where it identifies associations between variables in large databases. It identifies a couple of key attributes of a data object and predicts other attributes that are commonly associated with them. For example, given a large dataset of users; the algorithm identifies people who bought new houses and are most likely to purchase home appliances.
- Anomaly Detection
Just as how banks detect fraud by identifying the abnormal patterns in customer payment history such as multiple payments in multiple countries within a short period of time, the algorithm could be used to flag abnormal objects in a given dataset.
These algorithms are primarily used to improve the quality of images, videos, and scans by removing the noise as it uses an approach of taking inputs, summarizing it into machine language and recreating the inputs from the summarized machine code.
The main drawback of supervised machine learning would be classifying big data as the model would require to label large volumes of unstructured data before running the algorithm. The main drawback of the unsupervised model is that it cannot get precise information with regards to data sorting.
As you have learned above, supervised and unsupervised machine learning techniques have their own characteristics and advantages. Choosing a machine learning method depends on the requirement of the user, structure, and the volume of the data set. In the real world, both supervised and unsupervised approaches are applied, sometimes together by data scientists to effectively execute the machine learning tasks.https://www.completeconnection.ca/supervised-learning-vs-unsupervised-learning/Technology