Supervised Machine Learning: What is, Algorithms with Examples

What is Supervised Machine Learning?

Supervised Machine Learning is an algorithm that learns from labeled training data to help you predict outcomes for unforeseen data. In Supervised learning, you train the machine using data that is well “labeled.” It means some data is already tagged with correct answers. It can be compared to learning in the presence of a supervisor or a teacher.

Successfully building, scaling, and deploying accurate supervised machine learning models takes time and technical expertise from a team of highly skilled data scientists. Moreover, Data scientist must rebuild models to make sure the insights given remains true until its data changes.

How Supervised Learning Works

Supervised machine learning uses training data sets to achieve desired results. These data sets contain inputs and the correct output that helps the model to learn faster. For example, you want to train a machine to help you predict how long it will take you to drive home from your workplace.

Here, you start by creating a set of labeled data. This data includes:

  • Weather conditions
  • Time of the day
  • Holidays

All these details are your inputs in this Supervised learning example. The output is the amount of time it took to drive back home on that specific day.

How Supervised Machine Learning Works

You instinctively know that if it’s raining outside, then it will take you longer to drive home. But the machine needs data and statistics.

Let’s see some Supervised learning examples on how you can develop a supervised learning model of this example which help the user to determine the commute time. The first thing you requires to create is a training set. This training set will contain the total commute time and corresponding factors like weather, time, etc. Based on this training set, your machine might see there’s a direct relationship between the amount of rain and time you will take to get home.

So, it ascertains that the more it rains, the longer you will be driving to get back to your home. It might also see the connection between the time you leave work and the time you’ll be on the road.

The closer you’re to 6 p.m. the longer it takes for you to get home. Your machine may find some of the relationships with your labeled data.

Working of Supervised Machine Learning
Working of Supervised Machine Learning

This is the start of your Data Model. It begins to impact how rain impacts the way people drive. It also starts to see that more people travel during a particular time of day.

Types of Supervised Machine Learning Algorithms

Following are the types of Supervised Machine Learning algorithms:


Regression technique predicts a single output value using training data.

Example: You can use regression to predict the house price from training data. The input variables will be locality, size of a house, etc.

Strengths: Outputs always have a probabilistic interpretation, and the algorithm can be regularized to avoid overfitting.

Weaknesses: Logistic regression may underperform when there are multiple or non-linear decision boundaries. This method is not flexible, so it does not capture more complex relationships.

Logistic Regression:

Logistic regression method used to estimate discrete values based on given a set of independent variables. It helps you to predicts the probability of occurrence of an event by fitting data to a logit function. Therefore, it is also known as logistic regression. As it predicts the probability, its output value lies between 0 and 1.

Here are a few types of Regression Algorithms


Classification means to group the output inside a class. If the algorithm tries to label input into two distinct classes, it is called binary classification. Selecting between more than two classes is referred to as multiclass classification.

Example: Determining whether or not someone will be a defaulter of the loan.

Strengths: Classification tree perform very well in practice

Weaknesses: Unconstrained, individual trees are prone to overfitting.

Here are a few types of Classification Algorithms

Naive Bayes Classifiers

Naive Bayesian model (NBN) is easy to build and very useful for large datasets. This method is composed of direct acyclic graphs with one parent and several children. It assumes independence among child nodes separated from their parent.

Decision Trees

Decisions trees classify instance by sorting them based on the feature value. In this method, each mode is the feature of an instance. It should be classified, and every branch represents a value which the node can assume. It is a widely used technique for classification. In this method, classification is a tree which is known as a decision tree.

It helps you to estimate real values (cost of purchasing a car, number of calls, total monthly sales, etc.).

Support Vector Machine

Support vector machine (SVM) is a type of learning algorithm developed in 1990. This method is based on results from statistical learning theory introduced by Vap Nik.

SVM machines are also closely connected to kernel functions which is a central concept for most of the learning tasks. The kernel framework and SVM are used in a variety of fields. It includes multimedia information retrieval, bioinformatics, and pattern recognition.

Supervised vs. Unsupervised Machine learning techniques

Based On Supervised machine learning technique Unsupervised machine learning technique
Input Data Algorithms are trained using labeled data. Algorithms are used against data which is not labelled
Computational Complexity Supervised learning is a simpler method. Unsupervised learning is computationally complex
Accuracy Highly accurate and trustworthy method. Less accurate and trustworthy method.

Challenges in Supervised machine learning

Here, are challenges faced in supervised machine learning:

  • Irrelevant input feature present training data could give inaccurate results
  • Data preparation and pre-processing is always a challenge.
  • Accuracy suffers when impossible, unlikely, and incomplete values have been inputted as training data
  • If the concerned expert is not available, then the other approach is “brute-force.” It means you need to think that the right features (input variables) to train the machine on. It could be inaccurate.

Advantages of Supervised Learning

Here are the advantages of Supervised Machine learning:

  • Supervised learning in Machine Learning allows you to collect data or produce a data output from the previous experience
  • Helps you to optimize performance criteria using experience
  • Supervised machine learning helps you to solve various types of real-world computation problems.

Disadvantages of Supervised Learning

Below are the disadvantages of Supervised Machine learning:

  • Decision boundary might be overtrained if your training set which doesn’t have examples that you want to have in a class
  • You need to select lots of good examples from each class while you are training the classifier.
  • Classifying big data can be a real challenge.
  • Training for supervised learning needs a lot of computation time.

Best practices for Supervised Learning

  • Before doing anything else, you need to decide what kind of data is to be used as a training set
  • You need to decide the structure of the learned function and learning algorithm.
  • Gathere corresponding outputs either from human experts or from measurements


  • In Supervised learning algorithms, you train the machine using data which is well “labelled.”
  • You want to train a machine which helps you predict how long it will take you to drive home from your workplace is an example of Supervised learning.
  • Regression and Classification are two dimensions of a Supervised Machine Learning algorithm.
  • Supervised learning is a simpler method while Unsupervised learning is a complex method.
  • The biggest challenge in supervised learning is that Irrelevant input feature present training data could give inaccurate results.
  • The main advantage of supervised learning is that it allows you to collect data or produce a data output from the previous experience.
  • The drawback of this model is that decision boundary might be overstrained if your training set doesn’t have examples that you want to have in a class.
  • As a best practice of supervise learning, you first need to decide what kind of data should be used as a training set.