What is Supervised Machine Learning? Algorithm, Example
Updated on Feb 17, 2025 | 9 min read | 6.4k views
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Updated on Feb 17, 2025 | 9 min read | 6.4k views
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Machine learning is everywhere – from government agencies, retail services, and financial institutions to the healthcare, entertainment, and transport sectors. It is intricately associated with our day-to-day lives, be it Netflix or Amazon giving online recommendations or your smartphone unlocking with face detection technology, machine learning and artificial intelligence have gained momentum like never before.
With machine learning being one of the most popular tech trends now, it becomes imperative to know about one of the key approaches to creating artificial intelligence – supervised machine learning.
Supervised machine learning is a type of machine learning where a computer algorithm is trained using labelled input data and the computer, in turn, predicts the output for unforeseen data. Here, “labelled” means that some data will already be tagged with the correct answers to help the machine learn. In supervised learning, the input data fed to the computer works like a supervisor or teacher to train the machine to yield accurate results by detecting underlying patterns and correlations between the input data and the output labels.
There are different types of supervised learning algorithms to achieve specific results. Let us take a look at some of the most common types.
Classification algorithms use labelled training data to sort inputs into a given number of classes or categories. Here, the output variable is a category such as ‘Yes’ or ‘No’ and ‘True’ or ‘False.’ Categorising medical reports into positive (disease) or negative (no disease), or classifying movies into different genres are some instances where classification algorithms are applicable.
Regression models are used when there is a numerical relationship between the input and output variables. Regression algorithms that fall within the ambit of supervised learning include linear regression, non-linear regression, regression trees, polynomial regression, and Bayesian linear regression. Such models are primarily used to predict continuous variables such as speculating market trends, weather forecasting, or predetermining the click-through rates in online advertisements at specific times throughout the day.
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Neural network algorithms are used for interpreting sensory data, recognizing patterns, or clustering raw input. While this algorithm has several advantages, it can be pretty challenging to use a neural network when there too many observations. Popular real-life applications of neural networks include information extraction, text classification, speech and character recognition, multi-document summarization, language generation, and more.
Naive Bayes Classifiers is not a single algorithm but a collection of algorithms based on the Bayes’ Theorem. The standard principle underlying these algorithms is that every pair of classified features is independent of each other. Class labels are assigned using a direct acyclic graph comprising several children nodes and one parent node. Each child node is considered separate and independent from the parent. Popular real-life applications of the Naive Bayesian algorithm include spam filtering and sentiment analysis.
Decision trees are flowchart-like models containing conditional control statements to compare decisions and their possible consequences. A decision tree entails a tree-like graph where the internal nodes represent the point where we pick an attribute and ask a question, the leaf nodes represent the class labels or the actual output, and the edges stand for the answers to the questions.
Support Vector Machine (SVM) is based on the statistical learning theory given by Vap Nick and was developed back in 1990. In the simplest terms, support vector machines are a set of supervised learning methods used for regression, classification, and outlier detection. They are closely associated with the kernel network and find applications in diverse fields such as pattern recognition, bioinformatics, and multimedia information retrieval.
The random forest model consists of an ensemble of individual decision trees where each individual tree gives a class prediction, and the class with the maximum votes is the model’s prediction. The idea behind the concept of the random forest model is that a large number of relatively uncorrelated trees or models operating in an ensemble will produce more accurate predictions than any of the individual predictions. This is because the trees protect each other from independent errors.
Supervised learning involves training models using labelled datasets so that they can learn about each type of data. After the training is completed, the model is given test data to identify and predict the output.
Let us look at a simple example to clarify the concept further.
Say you are given a crate consisting of different kinds of vegetables. In the supervised machine learning approach, your first step will be to acquaint the machine with all the different vegetables one by one in this way:
Once you have trained the machine, you give it a separate vegetable from the crate (say, onion) and ask to identify it. Now, since the machine has already learned about the vegetables from previous data, it will classify the new object based on its shape and colour and confirm the result as an onion. In this way, the machine learns or trains from training data (crate containing vegetables) and applies the knowledge to new, unforeseen data (new vegetable).
Like the vegetable example we used above, let us see another supervised learning example to understand how it works.
Suppose we have a dataset consisting of various shapes such as triangles, squares, and pentagons. The first step is to train the model for each figure in the following way:
Once the training is complete, we test the model by using test data, and the job of the model would be to identify the shape based on the training knowledge. Hence, when the model finds a new shape, it classifies it on the basis of the number of sides and gives an output.
Needless to say, supervised learning has several advantages in implementing machine learning models. Some of its benefits are listed below:
No doubt, supervised learning algorithms are highly beneficial, especially with regard to their potential in addressing challenges in real-time. However, building a sustainable and efficient supervised learning model comes with its own set of challenges. So let’s take a look:
What are some of the best practices you should keep in mind before venturing out to begin a project using supervised machine learning? Take a look below.
It is worthwhile to finally talk about some of the best and most popular real-life examples of supervised machine learning.
It is one of the widely used supervised learning applications. It studies how individuals retain their biological knowledge, such as fingerprints, eye textures, etc. Intelligent devices such as mobile phones can detect biological data and verify individuals. This increases the system’s security. This is what is called what is supervised learning in AI.
The feature of speech recognition is used to convert spoken language into text. The technology is helpful in using machine learning and neural networks to process the audio data and convert the data into words that can be used in the business. The speech recognition feature can be used to convey the voice to the program and the voice identifies the person. There are various real-life gadgets that use the speech recognition feature such as Google Assistant, Alexa, Siri, etc. This is what is supervised learning in ML.
The feature of spam detection allows users to get spam emails detected. The tools are useful to detect fictitious or machine-based communications. Gmail has this feature, which has certain algorithms in place which is helpful to keep the inbox clean and keep only that information that is relevant to the users. This is what supervised machine learning.
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The latest market research report by Technavio titled Machine Learning Market by End-user and Geography – Forecast and Analysis 2020-2024 predicts that the global machine learning market size will witness a growth of US$ 11.16 billion during the forecast period 2020-2024. What’s more, the steady year-over-year increase in growth will fuel the market’s growth impetus.
Both present trends and future predictions indicate that machine learning is here to stay. Supervised learning algorithms are fundamental to any machine learning project that primarily involves classification and regression problems. Despite its challenges, supervised learning algorithms are the most useful for predicting outcomes based on experiences.
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