Supervised Machine Learning Examples (And How It Works)

Updated 10 August 2022

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Machine learning is a subdivision of AI (artificial intelligence) that uses programmed algorithms to make predictions. There are mainly two types of machine learning algorithms, namely supervised and unsupervised learning. If you are planning to interview for machine learning positions, you may benefit from knowing more about these concepts. In this article, we explore a few supervised machine learning examples and discuss what the machine learning algorithm is, how it works, its pros and cons and how it differs from unsupervised learning.

Related: Difference Between Supervised And Unsupervised Learning

Supervised Machine Learning Examples

Here are some of supervised machine learning examples models used in different business applications:

Image and object recognition

Supervised machine learning is used to locate, categorise and isolate objects from images or videos, which is useful when applied to different imagery analysis and vision techniques. The primary goal of image or object recognition is to identify the image accurately.

Example: We use the ML to recognise the image precisely as if it is the image of the plane or a car or if the image is of a cat or a dog.

Predictive analytics

Supervised machine learning models are widely used in building predictive analytics systems, which provide in-depth insights into different business data points. This enables the organisations to predict certain results using the output given by the system. It also helps business leaders to make decisions for the betterment of the company.

Example 1: We may use supervised learning to predict house prices. Data having details about the size of the house, price, the number of rooms in the house, garden and other features are needed. We need data about various parameters of the house for thousands of houses and it is then used to train the data. This trained supervised machine learning model can now be used to predict the price of a house.

Example 2: Spam detection is another area where most organisations use supervised machine learning algorithms. Data scientists classify different parameters to differentiate between official mail or spam mail. They use these algorithms to train the database such that the trained database recognise patterns in new data and classify them into spam and non-spam communication efficiently.

Sentiment analysis

Organisations can use supervised machine learning algorithms to predict customer sentiments. They use the algorithms to extract and categorise important information from large data sets like emotions, intent and context with little human interference. This model of supervised learning is also used to predict the sentiments of the text. This information is highly useful to gain insights about customer needs and help to improve brand-customer engagement efforts.

Example: Some organisations, especially e-commerce stores, often try to identify the sentiments of their customer via product reviews posted on their applications or websites.

What Is Supervised Machine Learning?

Supervised learning is a type of machine learning where well-labelled training data is used to train the machines. Machines use this data to make predictions and give the output. The "labelled" data implies some data is tagged with the right output. The training data that is sent as inputs to the machines work as a supervisor, and it teaches the machine to yield the correct output. This concept is like students learning under the supervision of a teacher. That is why it is called supervised machine learning.

Supervised machine learning algorithms aim to find a function to map the input data to the output data. Successfully building, scaling and deploying correct supervised learning models requires time and technical proficiency from a highly skilled team of data scientists. Also, data scientists may require to rebuild models to ensure the input given remains true until there is a change in its data.

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How Supervised Machine Learning Works?

In supervised learning, the accuracy of the algorithm is measured via loss function, making adjustments unless the error is sufficiently minimised. Supervised machine learning is divided into two problems during data mining. They are:

Classification

Classification uses different algorithms to precisely designate the test data into specific categories. It recognises distinct entities within a specific dataset and tries to make conclusions to label or define those entities. Some commonly used classification algorithms are SVM (Support Vector Machines), linear classifiers, k-nearest neighbour, random forest and decision trees. For example, we can use classification to predict if someone would be a loan defaulter.

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Regression

Regression is used to interpret the relationship between independent and dependent variables. It is used to draw projections, such as projecting the sales revenue in an estimated time frame. Some commonly used regression algorithms are logical regression, polynomial regression and linear regression. The output received by using regression has a probabilistic interpretation. For example, we can use regression to predict the price of a house.

Differences Between Supervised And Unsupervised Machine Learning

Below are a few of the differences between supervised and unsupervised machine learnings:

Unsupervised machine learning uses unlabelled data sets, whereas supervised machine learning uses well-labelled data sets.
In supervised learning, ML algorithms learn from the dataset by making multiple predictions and making adjustments for correct output. Unsupervised learning algorithms learn by themselves and discover any pattern of unlabelled data by themselves.
Supervised learning requires human intervention to make the data learn, whereas unsupervised learning requires minimal human intervention. The only time they are required to use humans is when they verify if the output is making sense.
Supervised learning algorithms are useful to make weather predictions, detect human sentiments and make pricing predictions. Unsupervised learning algorithms are useful for recommendation engines, customer personas, medical imaging or anomaly detection.
Supervised learning is comparatively simple, as it uses Python or R programming language, but unsupervised learning uses powerful tools to analyse a large volume of data. Also, unsupervised learning is computationally complex because of the vast amount of data it uses to predict the desired outcome.
Supervised machine learning is a more accurate method, whereas unsupervised learning is a comparatively less accurate method.
Supervised machine learning does the prediction for new data sets. The user is already aware of the expected output, but in unsupervised machine learning, insights are provided based on a huge amount of new data.

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Advantages Of Supervised Machine Learning

Here are a few advantages of supervised machine learning:

In supervised learning, you can collect data or produce output by using your previous experience.
This model allows you to optimise performance criteria by using experience.
You are completely aware of the number of classes in a training data set.
It allows you to understand the process of how the machine is learning to predict the output.
It helps to solve different real-world computation problems.
After completing training, it is not mandatory to store the training dataset in the memory; instead, you may maintain the decision boundary as a mathematical formula.

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Disadvantages Of Supervised Machine Learning

Here are some disadvantages of supervised machine learning:

To train the classifier, you may be required to choose a lot of examples from every class, otherwise, the accuracy of the output is impacted.
To classify a large amount of data is a challenge.
To train the data in supervised machine learning takes high computation time, which sometimes also tests the machine's efficiency.
Supervised learning cannot classify or cluster data by itself like unsupervised learning can.
It is not always possible to provide supervision for large data, so the machine may require to learn itself through training data.
Supervised learning's capability is limited in the sense that it is not capable of handling certain complex tasks in ML.
Supervised machine learning models need a lot of time to train the data and require expertise to create labelled data.

Challenges Faced In Supervised Machine Learning

There are a few common challenges you may face while working with supervised machine learning:

It is a challenge to pre-process the data and prepare the data for the input.
If incomplete values and unlikely and impossible values are sent as an input, the accuracy of the supervised learning model may decrease.
If the input is irrelevant to the model, it may give inaccurate output.
To label the data, an expert is important, but in the absence of one, the results could be inaccurate.
Since there is human intervention in supervised learning, there are chances of human error in datasets which may lead to incorrect learning of the algorithms.