Supervised vs. Unsupervised Learning: Differences Explained

With the progression of advanced machine learning inventions, strategies like supervised and unsupervised learning are floating more in the market. While both of these technologies are effective to tackle big data, splitting the difference between supervised and unsupervised learning within machine learning software paves the way for accurate product analysis.  

Supervised learning enables algorithms to predict unseen trends whereas unsupervised algorithms detect sentiments, anomalies or co-relations within the training data. 

As both ML algorithms depend on what kind of training data is fed to the model, utilizing data labeling software maps the exact need of labeling services for predictive modeling.

What is supervised learning? 

Supervised learning is a type of machine learning (ML) that uses labeled datasets to identify the patterns and relationships between input and output data. It requires labeled data that consists of inputs (or features) and outputs (categories or labels) to do so. Algorithms analyze the input information and then infer the desired output.

When it comes to supervised learning, we know what types of outputs we should expect, which helps the model determine what it believes is the correct answer. 

Supervised learning examples 

Some of the most common applications of supervised learning are: 

• Spam detection: As previously mentioned, email providers use supervised learning techniques to classify spam and non-spam content. This is done based on the features of each email (or input), like sender’s email address, subject line, and body copy, and the patterns that the model learns.  
• Object and image recognition: We can train models on a large dataset of labeled images, such as cats and dogs. Then, the model can extract features like shapes, colors, textures, and structures from the images to learn how to recognize these objects in the future.  
• Customer sentiment analysis: Companies can analyze customer reviews to determine their sentiment (e.g., positive, negative, or neutral) by training a model using labeled reviews. The model learns to associate specific words and features with different sentiments and can classify new customer reviews accordingly.
• Facial recognition: Labeled supervised data is used to predict foreign images from photos, videos or blueprints by matching it with the attributes in training data. Supervised machine learning model detects facial features and embeds vector representations to compare results and get the right confirmation.  
• Object recognition: Supervised learning is deployed to detect unwarranted objects or items to prevent obstruction in self-assist vehicles or devices. It requires minimal human oversight to detect unseen objects and predict the action that needs to be taken. 
• Biometric authentication: Because of increased accuracy and prediction, supervised algorithms can also tackle biometric authentication and predict employee credentials effectively. It leverages both training and test datasets to fine-tune output generation and authenticate individuals effectively. 
• Predictive modeling: Supervised learning is widely accepted strategy to forecast trends and strategies in commercial sector. Also known as predictive modeling, these examples include predicting the next quarter sales, analyzing marketing campaign data, forecasting budget trends, personalizing OTT feeds and so on. 
• Prescriptive analysis: In this technique, the input dataset is fine-tuned with external human inference that optimizes the quality of performed analysis and output generation. Accurate output leads to better prescriptive analysis which implies a more strategic and shaped memorandum for future course of action. 
• Optical character recognition: Supervised learning is effective in parsing and editing post data format (pdf) text as it predicts a correlation between dependent and independent variable and predict labels for text. Neural networks powered with supervised learning predict the nature, tone and criticality of text and categorize them in an editable format.  
• Voice recognition or speech recognition: This technique is prominent for dictating spoken words and converting it into a command for action. Based on the trained and tested audio dataset, users can process and convert voice commands into written or real-time automated workflows. 

Types of supervised learning classification

There are multiple methods of classification in supervised learning. For starters, the dataset is pre-processed, cleaned and evaluated for outliers. The labeled data establishes a strong correlation between a predicted variable and outcome variable.

Post data cleansing, the dataset is trained and tested on available labelled data to double check accuracy and classify unseen data. Based on prior training, here is how supervised learning is used to classify objects:

Binary classification

In binary classification, as mentioned earlier, the dataset is evaluated against hypothesis formation. It means that if A causes B, then the value of null hypothesis is true and if not, then alternative can be true. The A or B classification is defined as binary classification and there are five types of supervised learning classification

• Linear regression: Linear regression is a data analysis method which comprises an independent variable and a dependent variable that share a linear correlation are fed to the model to predict continuous outcomes. It can be performed with nominal, discrete and continuous data and these models can predict sales trends or forecasts. 
• Logistic regression: Logistic regression works with a larger datasets and streamlines variable's category probability to form good fit models. Based on probabilistic distribution, it assigns a particular category for the dependent variable.
• Decision trees: Decision trees follow a node-based technique to categorize data into attributes and understand statistical parameters to predict a specific outcome. The decision tree mechanism follows decision rules and deployed in predictive modeling and big data analysis.
• Time series: This technique is used to process sequential data like language, budget,  marketing metrics, stock prices or campaign attribution data. Some popular examples of time series models include recurrent neural networks, long short term memory (LSTM) models and so on. 
• Naive Bayes: Naive Bayes singles out attributes of labelled data and analyses individual features, assigns probability distribution and test's which category is the correct fit without overfitting the machine learning model.

Multiple class classification

In this supervised learning classification technique , the unseen data is assigned multiple (upto three) relevant categories or classes based on training of the model. There are three types of multiple class classification in supervised learning:

• Random forest: Random forest combines multiple decision trees to strengthen model testing and improve accuracy.  This algorithm is used to predict stronger co-relations, averaging predictions or predicting classes for large and diverse datasets. Some examples include weather forecast, match win projections, economic predictions and so on. 
• K-nearest neighbor (KNN): This algorithm is used to forecast the probability of a single data point as per the category of a heterogenous group of data points around it. K-nearest neighbor is a supervised learning technique that evaluates an "informative score" for "K" labels and calculates distances (like Euclidean) to predict the closest category.

Multiple label classification

Multiple label classification is a supervised technique where algorithms predict multiple labels as a good fit for independent variable. It combines the results of data analysis and human preprocessing to sift three or more relevant categories for output variable.

• Problem transformation:  With this strategy, you can convert multiple label outputs into a single most relevant output to solve confusion. Instead of multiple class values like dog, actor, mule, the algorithm assigns one relavant output. Problem transformation is essential for binary classification where we have one cause and one outcome. 
• Algorithm adaptation: With this technique, ML models can handle multiple classes effectively without overfitting the model. Examples include KNN, Naive Bayes, decision trees etc.
• Multiple label gradient boosting: This technique highlights the most relavant gradient or confidence interval of a variable belonging to a certain category. The gradients that are highlighted during testing phase are the labels that are assigned in the end.

Multiple label regression

Multiple label regression predicts multiple continuous output values for a single input data point. Unlike multiple label classification that assigns several categories to data, this approach models relationships between features within numerical values (like humidity or precipitation) and predict those values to forecast weather trends for activities like flight landing or takeoff, match delays and so on. 

Imbalanced classification

Imbalanced classification is defined as a supervised technique to handle uneven label classifications during the analysis process. Due to disparity in linear relationships, the end class prediction can become erroneous. Sometimes, it can also display the case of false positives in test data which inaccurately classifies unseen data.

What is unsupervised learning? 

Unsupervised learning is a type of machine learning that uses algorithms to analyze unlabeled data sets without human supervision. Unlike supervised learning, in which we know what outcomes to expect, this method aims to discover patterns and uncover data insights without prior training or labels. 

Unsupervised learning is used to detect correlations within datasets, relationships and patterns within variables and hidden trends and behaviour compositions to automate the data labeling process. Examples include anomaly detection, dimensionality reduction and so on.

Unsupervised learning examples 

Some of the everyday use cases for unsupervised learning include the following:

• Customer segmentation: Businesses can use unsupervised learning algorithms to generate buyer persona profiles by clustering their customers’ common traits, behaviors, or patterns. For example, a retail company might use customer segmentation to identify budget shoppers, seasonal buyers, and high-value customers. With these profiles in mind, the company can create personalized offers and tailored experiences to meet each group’s preferences.
• Anomaly detection: In anomaly detection, the goal is to identify data points that deviate from the rest of the data set. Since anomalies are often rare and vary widely, labeling them as part of a labeled dataset can be challenging, so unsupervised learning techniques are well-suited for identifying these rarities. Models can help uncover patterns or structures within the data that indicate abnormal behavior so these deviations can be noted as anomalies. Financial transaction monitoring to spot fraudulent behavior is a prime example of this.

Unsupervised learning clustering types

Unsupervised learning algorithms are best suited for complex tasks in which users want to uncover previously undetected patterns in datasets. Three high-level types of unsupervised learning are clustering, association, and dimensionality reduction. There are several approaches and techniques for these types.

Unsupervised learnng is used to detect internal relationships between unlabeled data points to predict an uncertainity score and take a stab at assigning correct category via machine learning processing.

Clustering in unsupervised learning

Clustering is an unsupervised learning technique that breaks unlabeled data into groups, or, as the name implies, clusters, based on similarities or differences among data points. Clustering algorithms look for natural groups across uncategorized data. 

For example, an unsupervised learning algorithm could take an unlabeled dataset of various land, water, and air animals and organize them into clusters based on their structures and similarities. 

Clustering algorithms include the following types: 

• K-means clustering:  K-means is a widely used algorithm for partitioning data into K-clusters that share similar characteristics and attributes. Each data point's distance from the centroid of these clusters is calculated. The nearest cluster is the category for that data point. This technique is best used for customer segmentation or sentiment analysis.
• Principal component analysis: Principal component analysis breaks down data into fewer components, also known as principal components. It is mainly used for dimensionality reduction, anomaly detection and spam reduction. 
• Gaussian mixture models: This is a probablistic clustering models where input data is scrutinized for inward correlations, patterns and trends. The algorithm assigns a probability score for each datapoint and detects the right category. This technique is also known as soft clustering, as it gives a probability inference to a data point.

Association in unsupervised learning clustering

In this unsupervised learning rule-based approach, learning algorithms search for if-then correlations and relationships between data points. This technique is commonly used to analyze customer purchasing habits, enabling companies to understand relationships between products to optimize their product placements and targeted marketing strategies. 

Imagine a grocery store wanting to understand better what items their shoppers often purchase together. The store has a dataset containing a list of shopping trips, with each trip detailing which items in the store a shopper purchased. 

The store can leverage association to look for items that shoppers frequently purchase in one shopping trip. They can start to infer if-then rules, such as: if someone buys milk, they often buy cookies, too. 

Then, the algorithm could calculate the confidence and likelihood that a shopper will purchase these items together through a series of calculations and equations. By finding out which items shoppers purchase together, the grocery store can deploy tactics such as placing the items next to each other to encourage purchasing them together or offering a discounted price to buy both items. The store will make shopping more convenient for its customers and increase sales. 

Dimensionality reduction 

Dimensionality reduction is an unsupervised learning technique that reduces the number of features or dimensions in a dataset, making it easier to visualize the data. It works by extracting essential features from the data and reducing the irrelevant or random ones without compromising the integrity of the original data.

Choosing between supervised and unsupervised learning 

Selecting the suitable training model to meet your business goals and intent outputs depends on your data and its use case. Consider the following questions when deciding whether supervised or unsupervised learning will work best for you: 

• Are you working with a labeled or unlabeled dataset? What size dataset is your team working with? Is your data labeled? Or do your data scientists have the time and expertise to validate and label your datasets accordingly if you choose this route? Remember, labeled datasets are a must if you want to pursue supervised learning.
• What problems do you hope to solve?  Do you want to train a model to help you solve an existing problem and make sense of your data? Or do you want to work with unlabeled data to allow the algorithm to discover new patterns and trends? Supervised learning models work best to solve an existing problem, such as making predictions using pre-existing data. Unsupervised learning works better for discovering new insights and patterns in datasets. 

Supervised vs. unsupervised learning: key differences 

Here is a summary of key differentiators between supervised and unsupervised learning that explains the parameters and applications of both types of machine learning modeling:

Supervise or unsupervise, as you see fit

Whether you choose an unsupervised or supervised technique, the end goal should be to make the right prediction for your data. While both strategies have their benefits and anomalies, they require different resources, infrastructure, manpower and data quality.  Both supervised and unsupervised learning are topping the charts in their own domain, and the future of industries bank on them. 

Learn more about machine learning models and how to they train, segment and analyze data to predict successful outcomes.