Machine Learning: Supervised VS Unsupervised

Machine learning is an AI-driven technology , wherein we give machines access to data and allow them to interpret it into output. Every day, the world becomes ‘smarter’ and to keep up with customer expectations, companies are increasingly turning towards machine learning algorithms to make things simpler and efficient.

Machine learning technologies can be seen in use in end-user devices (for example, facial recognition for unlocking smartphones) or detecting credit card frauds ( triggering an alert on detecting an unusual purchase). Since this technology is used in many real-world applications, having a basic understanding of it is beneficial.

There are two fundamental approaches to train artificial intelligence (AI) and machine learning systems: Supervised learning and Unsupervised learning. The primary distinction is that one uses labeled data to help predict outcomes, whilst the other does not. However, there are several other notable differences between the two approaches, as well as crucial areas where one surpasses the other.

This post will clarify the basic differences between supervised learning and unsupervised learning.

What is Supervised Learning?

Supervised learning is a machine learning technique wherein the algorithm is trained on labeled datasets. These datasets are specifically designed to train or ‘supervise’ systems in properly categorizing data or predicting outcomes.

Supervised machine learning algorithms will continue to develop even after they have been implemented, discovering new patterns and correlations as they train themselves on new data.

When it comes to data mining, supervised learning may be divided into two categories of problems: Classification and regression:

1. Classification

Classification algorithm classifies input data to properly categorize test data into a number of classes or categories, such as separating apples from oranges. In the real world, supervised learning algorithms may be used to segregate spam emails from your inbox in a different folder. Classification algorithms include linear classifiers, support vector machines, decision trees, and random forest.

2. Regression

Another form of supervised learning approach is regression, which utilizes an algorithm to determine the connection between dependent and independent variables. Linear regression, non-linear regression, regression trees, polynomial regression, and Bayesian linear regression are examples of regression algorithms.

These models are mostly used to forecast continuous variables like market trends, weather forecasting, etc. Regression models are also useful for predicting numerical values based on several different data sources, such as sales revenue forecasts for a certain company.

What is Unsupervised Learning?

Unsupervised learning analyses and clusters unlabeled data sets using machine learning techniques. These algorithms find hidden patterns in data without requiring human interaction (thus the term ‘unsupervised’).

Unsupervised learning models are used to perform three major tasks: clustering, association, and dimensionality reduction:

1. Clustering

Clustering is a data mining technique used to group unlabeled data based on similarities and differences in such a way that those with the most similarities stay in one group while those with less or no similarities stay in another.

There can be different types of clustering, which include- Hierarchical clustering, k-means algorithm, Principal Component Analysis, Singular Value Decomposition and Independent Component Analysis.

For example, K-means clustering algorithms divide comparable data points into groups, where the K value reflects the size and granularity of the grouping. This approach is useful for market segmentation, image compression, and other purposes.

2. Association

An association rule is a type of unsupervised learning approach which is used to find relationships between different variables in a large database. It identifies the group of items that appear together in the data set.

These methods are commonly employed in Market basket analysis and recommendation engines, such as ‘Customers Who Bought This Item Also Bought’ suggestions.

3. Dimensionality Reduction

Dimensionality reduction is a learning approach that is utilised when the number of features (or dimensions) in a given data set is excessively large. It reduces the amount of data inputs to a reasonable level while maintaining data integrity. This approach is frequently employed in data pre-processing, such as when auto-encoders remove noise from visual data to improve picture quality.

Supervised VS Unsupervised Learning: Labeled data

The use of labelled datasets is the primary difference between the two approaches. Simply put, supervised learning algorithms utilize labeled input and output data, whereas unsupervised learning algorithms do not.

In supervised learning, the algorithm ‘learns’ from the training data set by generating predictions on the data continuously and modifying to predict the correct response. While supervised learning models are more accurate than unsupervised learning models, they require human involvement to label the data properly at the beginning.

A supervised learning model, for example, can estimate how long your commuting time would be based on the time of day, weather conditions, and other factors. But first, you must train it to know that rainy weather increases travel time, or during the peak office hours commuting can take longer than usual.

Unsupervised learning models, on the other hand, function independently to discover the intrinsic structure of unlabeled data. It should be noted that they still require some human interaction in order to validate output variables.

An unsupervised learning model, for example, can recognise that online customers frequently purchase groups of items at the same time. A data analyst, on the other hand, would need to confirm that it makes sense for a recommendation engine to group baby clothing with an order of diapers, applesauce, and sippy cups.

Other Key Differences Between Supervised and Unsupervised Learning

1. Objective

The objective of supervised learning is to predict outcomes for fresh data. You know what kind of outcomes to expect from the beginning. The objective of an unsupervised learning algorithm is to extract insights from vast amounts of new data. The machine learning algorithm discovers what is unique or intriguing about the data set.

2. Applications

Applications of supervised learning include spam detection, sentiment analysis, weather forecasting, and pricing predictions, among others. Unsupervised learning, on the other hand, is ideal for anomaly detection, recommendation engines, and medical imaging.

3. Complexity

Supervised learning is a basic approach for machine learning that is generally computed using programmes such as R or Python. Unsupervised learning requires the use of powerful tools for dealing with large volumes of unclassified data. Because they require a large training set to get the desired results, unsupervised learning models are computationally difficult.

4. Drawbacks

Supervised learning models can take a long time to train, and the labels for input and output variables require knowledge. Meanwhile, unsupervised learning algorithms can produce dramatically inaccurate results unless the output variables are validated by humans.

The Best of Both Worlds: Semi-Supervised Learning

Semi-supervised learning is a middle ground in which a training data set contains both labelled and unlabeled data. It’s especially beneficial when it’s difficult to extract meaningful characteristics from data — and when there’s a lot of it.

Semi-supervised learning is appropriate for medical imaging, because a small quantity of training data may result in a significant increase in accuracy. A radiologist, for example, may label a small fraction of CT images for tumours or diseases, allowing the system to more accurately predict which individuals may require additional medical attention.

In today’s increasingly competitive environment, machine learning is enabling organizations to accelerate their digital transformation and move into an age of automation. With the assistance of Machine Learning Algorithms, AI was able to progress beyond simply performing the tasks it was programmed to do.

Machine learning models are a great tool for gaining data insights that may be used to improve our world. To understand more about the specific algorithms used with supervised and unsupervised learning, we recommend reading our prior blogs that include in-detail information about different types of ML algorithms.