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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
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Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
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Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Demystifying the Difference Between Multi-Class and Multi-Label Classification Problem Statements in Deep Learning

Chirag Goyal
Chirag Goyal Last Updated : 02 Jan, 2025
6 min read

While solving the classification problem statements using Deep Learning, we may come up with mainly the following two types of classification tasks:

  • Multi-Class Classification
  • Multi-Label Classification

As a short introduction, In multi-class classification, each input will have only one output class, but in multi-label classification, each input can have multi-output classes.

Multi-Class and Multi-Label Classification image

                                                    Image Source: Link

But these terms i.e, Multi-class and Multi-label classification can confuse even the intermediate developer. So, In this article, I have tried to give you a clear and easy intuition with examples of these terms in a detailed manner. If you are a Data Science Enthusiast, then read this article completely and accelerate your Data Science Journey.

This article was published as a part of the Data Science Blogathon

Table of contents

What is Binary Classification?

In binary classification problem statements, any of the samples from the dataset takes only one label out of two classes.

For example, Let’s see an example of small data taken from amazon reviews data set.

Binary Classification

Table Showing an Example of Binary Classification Problem Statement

                                                       Image Source: Link

If we carefully look into the table, we will see that we can only classify the review as either positive or negative i.e, only two possible target outcomes. So, this is an example of a binary classification problem statement.

What is Multi-Class Classification?

To understand multi-class classification, we will first define what multi-class means and identify the differences between multi-class and binary-class.

Multi-class vs. binary-class is the issue of the number of classes your classifier will be modeling. Theoretically, a binary classifier is much less complicated than a multi-class classifier, so it is essential to make this distinction.

For example, the Support Vector Machine (SVM) trivially can learn one hyperplane to split two classes, but 3 or more classes make it complex. In neural networks, we usually use the Sigmoid Activation Function for binary classification tasks while on the other hand, we use the Softmax activation function for multi-class as the last layer of the model.

For multi-class classification, we need the output of the deep learning model to always give exactly one class as the output class.

For example, if we create an animal classifier that distinguishes between dogs, rabbits, cats, and tigers, we can only select one of these classes each time.

Multi-Class Classification

                                                       Image Source: Link

To ensure we select only one class each time, we apply the Softmax Activation Function at the last layer and use log loss to train the model.

Therefore, for a given dataset, any of the samples that come from the dataset takes only one label out of the number of classes. Let’s see an example of small data taken from the movies reviews dataset.

Multi-Class Classification table

Table Showing an Example of Multi-Class Classification Problem Statement

                                                     Image Source: Link    

If we carefully look into the table, we will see that we can only classify the movie rating from 2 to 5 i.e, each movie will have only one label (2, 3, 4, or 5). This means samples can have more than two possible target outcomes. So, this is an example of a multi-class classification problem statement.

What is Multi-Label Classification?

To understand multi-label classification, firstly we will understand what is meant by multi-label, and find the difference between multi-label and binary-label.

Multi-label vs. single-label is the matter of how many classes an object or example can belong to. In neural networks, when single-label is required, we use a single softmax layer as the last layer, learning a single probability distribution that ranges over all classes. In the case where multi-label classification is needed, we use multiple sigmoids on the last layer and thus learn a separate distribution for each class.

In certain problems, each input can have multiple, or even none, of the designated output classes. In these cases, we go for the multi-label classification problem approach.

For example, If we are building a model which predicts all the clothing articles a person is wearing, we can use a multi-label classification model since there can be more than one possible option at once.

Multi-Label Classification

                                                       Image Source: Link

Therefore, for a given dataset, any of the samples that come from the dataset takes more than one label out of the number of available classes. Let’s see a toy example.

Multi-Label Classification

Table Showing an Example of Multi-Label Classification Problem Statement

                                                 Image Source: Link

If we carefully look into the table, we will see that the movie may take more than one genre i.e, the movie could be comedy and Fantasy at the same time. This means samples can have more than two possible labels. So, this is an example of a multi-label classification problem statement.

Real-Life Example to Understand the Difference between Multi-Class and Multi-Label Classification

Consider the following real-life example to understand the difference between these two types of classification. To understand the exact difference, I hope the below image makes things quite clear. Let’s try to understand it.

Real-Life Example

                                                 Image Source: Link

As you can know the general information that for any movie, the organization named Central Board of Film Certification, issues a certificate depending on the contents of the movie.

For example, if you look in the above image, then you may see that this movie has been rated as ‘U/A’ (meaning ‘Parental Guidance for children below the age of 12 years) certificate. This is not the only type of certificate but there are other types of certificates classes such as,

  • ‘A’ (Restricted to adults), or
  • ‘U’ (Unrestricted Public Exhibition),

While categorizing the movies, we can only assign each movie one of the three types of certificates. In short, multiple categories (i.e., multiple certificates) exist, but we assign each instance only one certificate at a time. Therefore, we categorize such problems under the multi-class classification problem statement.

Again, if you examine the image closely, you will see that this movie falls into the comedy and romance genres. But this time there is a difference that each of the movies can fall into one or more different sets of categories (i.e, have more than one genre). Therefore, we can assign each instance multiple categories (i.e., multiple genres), categorizing these problems under the multi-label classification problem statement, where each sample has a set of target labels.

Great! after understanding this example properly, now you can easily distinguish between multi-label and multi-class problem statements. Congratulations on this! 😊

Test Your Knowledge (Interview Quiz)

In this section, I have given some questions to test your knowledge regarding the topic which we have discussed in this article.

Question-1: Multi-class classification problems have multiple categories but each instance is assigned only once.

  • True
  • False

Question-2: Multi-label classification problems have each instance can be assigned with multiple categories or a set of target labels.

  • True
  • False

Note: Feel free to discuss the answer to these questions in the comment box below!

Self Learning Resource

If you want to know how to solve multiclass and multilabel classification problem statements, you can refer to the following link- Multiclass Classification using SVM

Other Blog Posts by Me

You can also check my Previous Data Science Blog posts.

LinkedIn

Here is my Linkedin profile in case you want to connect with me. I’ll be happy to be connected with you.

For any queries, you can mail me on [email protected]

Thanks for reading!

Analytics Vidhya does not own the media shown in this article, and the author uses it at their discretion.

Chirag Goyal
Chirag Goyal

I am a B.Tech. student (Computer Science major) currently in the pre-final year of my undergrad. My interest lies in the field of Data Science and Machine Learning. I have been pursuing this interest and am eager to work more in these directions. I feel proud to share that I am one of the best students in my class who has a desire to learn many new things in my field.

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Really helpful article Difference Between Multi-Class and Multi-Label Classification Problem! You really have great stuff on this topic! Thanks for the valuable information...

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