Supervised machine learning is a type of machine learning in which the model is trained on labeled data. In other words, the predictions of such models depend on the quality of the data annotation process.
To help you fully understand supervised learning models, I will answer the following questions in this guide:
- What is supervised machine learning?
- How does supervised learning differ from unsupervised learning?
- How does supervised machine learning work?
- What are the use cases of supervised machine learning?
So if you want to understand the supervised learning process in detail, you are in the right place.
What is Supervised Machine Learning
“Supervised machine learning is a type of ML where a model is trained using labeled data.”
To understand this better, imagine you want to train an ML model to distinguish between cats and dogs. Here’s the catch: the model cannot learn on its own from raw, unlabeled images. You first need to provide a labeled dataset, where each image is clearly marked as a cat or a dog.
And then comes the main part: you need to feed the model the labeled dataset, the one that clearly labels cats, dogs, and other objects. Using this labeled image data as a reference, the model learns the patterns that help differentiate one animal from the other.
Later, when it is given completely new/unseen data, it can accurately predict whether the image contains a cat or a dog. In simple terms, this is exactly how supervised learning works: by learning from labeled examples.
7 Key Features of Supervised Learning Algorithms
Let’s discuss some common attributes that define supervised learning models. This will also give you an idea of how supervised learning algorithms actually work.
- They use training data to learn the relationship between inputs and outputs.
- Each data point in the training data is paired with a correct label/value. These labels act as the “Ground Truth” for a supervised learning model. [add link to “What is Ground Truth Data”]
- During training, supervised learning models compare their predictions with known target values to improve the accuracy of their predictions.
- The collected dataset, in most cases, is divided into three types: training data, validation data, and testing data.
- Since the classes are already known, it is very easy to judge their performance.
- The output is either a class label or a continuous numerical value.
- Supervised learning requires labeled data, which may involve human data labeling. When large datasets are involved, the process can become both time-consuming and costly.
Main Types of Supervised Learning Models
Supervised machine learning can be broadly divided into two main types:
1. Classification
As the name shows, classification algorithms assign class labels to input data points to make predictions or decisions. When your goal is to divide data into predefined classes or categories, a classification model is the best option.
Note: The dataset might contain images, video files, audio files, text documents, or any other type of data.
Example
A supervised classification algorithm can be used to categorize spam and legitimate emails by recognizing their patterns. Such ML-based software can easily identify spam emails and protect you from online scams.
Algorithms Used for Classification
- Decision Tree Classifier
- Logistic Regression
- Support Vector Machine (SVM)
- K-Nearest Neighbor Classifier
- Random Forest Classifier
- Neural Networks
2. Regression
Let’s move on to the second major type of supervised learning. Regression is used when the goal is to predict a continuous numerical value rather than assign data to a predefined class.
These models learn the relationship between input features and a numerical target to make predictions on new data. While the output of a classification algorithm is in the form of categorization, a regression model predicts a specific value.
Example
Suppose you want to simply predict the value of a house. Here, classification algorithms won’t help you because you are looking for a continuous numerical value.
A regression algorithm can provide you with an approximate house value by extracting features from training data, such as the size, location, age, number of rooms, and the neighbouring facilities.
Algorithms Used for Regression
- Decision Tree Regression
- Random Forest Regression
- Linear Regression (don’t confuse this with logistic regression)
- Polynomial Regression
- Support Vector Regression (SVR)
| Feature | Classification | Regression |
| Goal | To assign labels/categories to input data | To predict continuous, numerical values rather than categorization |
| How it Works | Learns patterns to place data points into separate groups | Using historical data, it predicts a specific point |
| Output Type | Discrete classes | Continuous values |
| Common Algorithms | -> Logistic Regression -> Decision Tree Classifier -> Support Vector Machines -> Naive Bayes | -> Linear Regression -> Polynomial Regression -> Support Vector Regression -> Random Forest Regression |
| Example | Predicting whether an email is spam or legitimate | Predicting house prices or temperature trends |
| Best For | Spam detection, fraud detection | Trend detection, weather, and stock value prediction |
Supervised vs Unsupervised Machine Learning: What’s the Difference
Now, you know exactly what supervised learning refers to, and how it depends on labeled data for training. But some questions are still unanswered, such as:
- What is unsupervised learning?
- How is the unsupervised learning approach different from supervised learning?
Instead of going into the technical details that might sound confusing to you, I have compiled a table to compare both learning approaches in simpler words:
| Feature | Supervised Learning | Unsupervised Learning |
| Definition | An ML approach that needs labeled data for training | Uses unlabeled or raw data to discover patterns |
| Training Data | Labeled data required | Uses unlabeled data |
| Types | -> Classification -> Regression | -> Clustering -> Association Rule Learning |
| Pros | Can handle both classification tasks and continuous value prediction | Discovers hidden patterns without manual labeling |
| Level of Simplicity | Very simple | Computationally complex |
| Level of Accuracy | Accurate predictions | Generally less accurate in comparison |
To make things even simpler for you, here’s a brief summary of this table:
- Supervised machine learning uses labeled data to make accurate predictions. While it is known for accurate predictions, it relies heavily on high-quality labeled datasets.
- Unsupervised machine learning works with unlabeled data to discover hidden patterns. But keep in mind that this approach is computationally complex.
What are the Use Cases of Supervised Machine Learning
From healthcare to the e-commerce industry, supervised learning helps business owners make informed decisions. Here are some of the applications of the supervised learning approach,
1. Fraud Detection in Bank Transactions
Fraud detection is one of the most practical real-world applications of supervised machine learning. In such an AI-based system, a classification-based supervised learning model is trained using labeled transaction data, where each transaction is marked as fraudulent or legitimate.
Once this model is trained using high-quality labeled data, you can trust it to identify fraudulent transactions and separate them from legitimate ones. This is how supervised ML plays a role in the finance/banking sector.
2. Spam Email Detection
Another common use case of supervised machine learning is spam email detection. In this approach, the supervised ML model is trained using a labeled dataset, where each email is marked as either spam or legitimate.
Based on this training process, the model learns to recognize patterns that separate spam emails from genuine ones.
Once trained, the model can accurately classify new incoming emails as spam or legitimate, helping users avoid phishing attempts, scams, and other potential risks. This way, customers are timely notified and protected from phishing attacks.
3. Accurate Medical Diagnosis
Another field where supervised learning is increasingly used is the healthcare industry. An ML model is properly trained using labeled clinical data, such as patient symptoms, test results, etc. As a result, such models can help doctors predict likely diagnoses.
However, it’s important to remember that even the best models are not replacements for doctors. The important thing here is that such predictions must be verified by medical professionals, as ML models are not always 100% accurate.
Final Thoughts
While the supervised learning approach sounds like a great option, the accuracy of such models depends on the data labeling process. And when you’re working with large datasets, manually labeling all that data can become a mess.
To avoid wasting resources and money on manual data labeling, you can partner with a reliable annotation outsourcing company
At Annotation, we provide multiple high-quality data annotation services to 100+ businesses. We offer data labeling services starting from $5 per annotation hour, which is much more cost-effective than hiring an in-house team.
Get in touch with us today, and let us help you build accurate supervised ML models.