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What is Supervised Machine Learning? Algorithm, Example

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. 

What is 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. 

Types of Supervised Learning Algorithms

There are different types of supervised learning algorithms to achieve specific results. Let us take a look at some of the most common types.

1. Classification

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.

2. Regression

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. 

3. Neural Networks

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.

4. Naive Bayesian Model

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.

5. Decision Trees

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.

6. Support Vector Machine

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.

7. Random Forest Model

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.

How Does It Work?

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:

  • If the object is like a bulb and purplish-pink, it will be labelled as – Onion.
  • If the object is leafy and green in colour, then it will be labelled as – Spinach.

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:

  • If the shape has three sides, then it will be labelled as – Triangle
  • If the shape has four equal sides, then it will be labelled as – Square
  • If the shape has five sides, then it will be labelled as – Pentagon

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.

Advantages and Challenges

Needless to say, supervised learning has several advantages in implementing machine learning models. Some of its benefits are listed below:

  • Supervised learning models can accurately predict outputs based on prior experiences.
  • Supervised learning helps to optimise performance using experience.
  • Supervised learning gives us a clear and precise idea about the classes of objects.
  • Last but not least, supervised learning algorithms are incredibly crucial for solving various real-world problems and find applications in diverse sectors.

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:

  • The entire process of training supervised learning models is a time-consuming process.
  • Supervise learning models often require a certain level of expertise and resources to structure and function accurately.
  • In contrast to unsupervised learning models, supervised learning models cannot classify or cluster data on their own.
  • The chances of human errors creeping into datasets are quite high, which can lead to algorithms training incorrectly.

Best Practices With Examples

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.

  • Make sure you are clear about the kind of data you will use as the training dataset.
  • Collect corresponding outputs either from standard measurements or human experts.
  • Decide the structure of the learning algorithm.

It is worthwhile to finally talk about some of the best and most popular real-life examples of supervised machine learning. 

  • Predictive analysis: A widespread use case of using supervised learning models for predictive analysis is providing meaningful and actionable insights into various business data points. As a result, business enterprises can foresee certain outcomes based on a given output variable to justify and back up decisions.
  • Object and image recognition: Supervised learning algorithms find use in locating and classifying objects in images and videos – a frequent requirement in image analysis and various computer vision techniques.
  • Spam detection: Spam detection and filtering techniques use supervised classification algorithms to train databases so that they can recognise patterns in new data for effective segregation of spam and non-spam emails. 
  • Sentiment analysis: A great way to boost brand engagement efforts is to understand customer interactions. Supervised machine learning can help in this regard by extracting and classifying critical information from large datasets such as customer’s emotions, intents, preferences, etc.

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Conclusion

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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