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Binary and Multiclass Classification in Machine Learning

  • Utsav Mishra
  • May 16, 2021
  • Machine Learning
Binary and Multiclass Classification in Machine Learning title banner

Science and technology have significantly helped the human race to overcome most of its problems. From making people fly in the air to helping them in managing traffic on roads, science has been present everywhere. 

 

Not even a single field is there, where science isn’t involved. From live-saving machinery to time-saving applications, it is present everywhere. But, in today’s world, the place where science is involved the most nowadays is in technology. 

 

Every application we have on the phone uses some kind of science. For example, when a map application tells us the speed of our travel, it simply uses the concept of distance covered till the point/ time taken. It’s simple science which when combined with technology gives us all kinds of fruitful results.

 

When it comes to technology and science, we can’t move ahead without talking about the latest technologies available.

 

One of the latest technologies that has revolutionized the tech world completely is “machine learning”. Let’s start our notch discussion with machine learning and then dive deep into the binary classification.


 

What is Machine Learning

 

Machine learning is the science of teaching and educating the computer i.e. a machine to behave and act like a human and improve itself over time. This is done by feeding the machine with data and information in the form of real-world interactions, it can be done through coding and feeding the machine with the desired data. 

 

Through Machine learning algorithms, the device learns from the data provided and acts accordingly in the situation provided. It is basically a part of artificial intelligence that provides computers the ability to learn through data and observations. 

 

Supervised Machine Learning 

 

Supervised machine learning is a type of machine learning where a specifically known dataset is provided to make predictions. In the dataset, there are two types of variables, input variable(X), output variable(Y).  

 

In this, a supervised learning algorithm builds a model where the response variable is used over the known dataset, to check the accuracy of the model.

 

As a part of supervised machine learning, classification has achieved a speculations rise. 


 

Definition of Classification

 

In machine learning, Classification, as the name suggests, classifies data into different parts/classes/groups. It is used to predict from which dataset the input data belongs to.

 

For example, if we are taking a dataset of scores of a cricketer in the past few matches, along with average, strike rate, not outs etc, we can classify him as “in form” or “out of form”. 

 

Classification is the process of assigning new input variables (X) to the class they most likely belong to, based on a classification model, as constructed from previously labeled training data. 

 

Data with labels is used to train a classifier such that it can perform well on data without labels (not yet labeled). This process of continuous classification, of previously known classes, trains a machine. If the classes are discrete, it can be difficult to perform classification tasks.


 

Types of Classification

 

There are two types of classifications;

 

  • Binary classification

  • Multi-class classification


 

Binary Classification

 

It is a process or task of classification, in which a given data is being classified into two classes.  It’s basically a kind of prediction about which of two groups the thing belongs to. 

 

Let us suppose, two emails are sent to you, one is sent by an insurance company that keeps sending their ads, and the other is from your bank regarding your credit card bill. The email service provider will classify the two emails, the first one will be sent to the spam folder and the second one will be kept in the primary one. 

 

This process is known as binary classification, as there are two discrete classes, one is spam and the other is primary. So, this is a problem of binary classification.

 

Binary classification uses some algorithms to do the task, some of the most common algorithms used by binary classification are .

 

 

The video below explains the concept of binary classification more clearly



 

Term Related to binary classification 

 

  1. PRECISION

 

Precision in binary classification (Yes/No) refers to a model's ability to correctly interpret positive observations. In other words, how often does a positive value forecast turn out to be correct? We may manipulate this metric by only returning positive for the single observation in which we have the most confidence.

 

  1. RECALL

 

The recall is also known as sensitivity.  In binary classification (Yes/No) recall is used to measure how “sensitive” the classifier is to detecting positive cases. To put it another way, how many real findings did we “catch” in our sample? We may manipulate this metric by classifying both results as positive.

 

  1. F1 SCORE

 

The F1 score can be thought of as a weighted average of precision and recall, with the best value being 1 and the worst being 0. Precision and recall also make an equal contribution to the F1 ranking. 


 

Multiclass Classification

 

Multi-class classification is the task of classifying elements into different classes. Unlike binary, it doesn’t restrict itself to any number of classes. 

 

Examples of multi-class classification are 

  • classification of news in different categories, 

  • classifying books according to the subject, 

  • classifying students according to their streams etc.

 

In these, there are different classes for the response variable to be classified in and thus according to the name, it is a Multi-class classification.

 

Can a classification possess both binary or multi-class? 

 

Let us suppose we have to do sentiment analysis of a person, if the classes are just “positive” and “negative”, then it will be a problem of binary class. But if the classes are “sadness”, happiness”, “disgusting”, “depressed”, then it will be called a problem of Multi-class classification.


 

Binary vs Multiclass Classification


 

Parameters

Binary classification 

Multi-class classification

No. of classes

It is a classification of two groups, i.e. classifies objects in at most two classes.

There can be any number of classes in it, i.e., classifies the object into more than two classes.

Algorithms used 

The most popular algorithms used by the binary classification are-

 
  • Logistic Regression

  • k-Nearest Neighbors

  • Decision Trees

  • Support Vector Machine

  • Naive Bayes

Popular algorithms that can be used for multi-class classification include:

  • k-Nearest Neighbors

  • Decision Trees

  • Naive Bayes

  • Random Forest.

  • Gradient Boosting

Examples 

Examples of binary classification include- 

  • Email spam detection (spam or not).

  • Churn prediction (churn or not).

  • Conversion prediction (buy or not).

Examples of multi-class classification include:

  • Face classification.

  • Plant species classification.

  • Optical character recognition.


 

Conclusion

 

Looking at the types of classification and the basics of machine learning, we reach the conclusion that the science involved in it is the key to future technology. Be it AI or ML, both things have parts under them that are a lot more important than they look like. 

 

(Recommended blog: Machine Learning Tutorial)

 

One such thing was classification, used daily in our lives, who knew that computers used these simple processes to do complex tasks. As we went deeper we found out a lot more exciting things. But that’s the thing about science, it doesn’t stop the excitement, instead, there is always some more to explore.

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