EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login

Classification Algorithms

Home » Data Science » Data Science Tutorials » Machine Learning Tutorial » Classification Algorithms

Classification Algorithms

Introduction to Classification Algorithms

This article on classification algorithms puts an overview of different classification methods commonly used in data mining techniques with different principles. Classification is a technique which categorizes data into a distinct number of classes and in turn label are assigned to each class. The main target of classification is to identify the class to launch new data by analysis of the training set by seeing proper boundaries. In a general way, predicting the target class and the above process is called classification.

For instance, the hospital management records the patient’s name, address, age, previous history of the patient’s health to diagnosis them, this helps to classify the patients. They can be characterized into two phases: a learning phase and an evaluation phase. Learning phase models the approach base don a training data whereas the evaluation phase predicts the output for the given data. We could find their applications in email spam, bank loan prediction, Speech recognition, Sentiment analysis. The technique includes mathematical function f with input X and output Y.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Explain Classification Algorithms in Detail

Classification can be performed on both structured and unstructured data. Classification can be categorized into

Explain Classification Algorithms in Detail

  1. Naive Bayes classifier
  2. Decision Trees
  3. Support Vector Machine
  4. Random Forest
  5. K- Nearest Neighbors

1. Naive Bayes classifier

It’s a Bayes’ theorem-based algorithm, one of the statistical classifications, and requires few amounts of training data to estimate the parameters also known as probabilistic classifiers. It is considered to be the fastest classifier, highly scalable, and handles both discrete and continuous data. This algorithm used to make a prediction in real-time. There are different types of naive classifier, Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Gaussian naive.

Bayesian classification with posterior probabilities is given by

classification algorithms formula

Where A, B are events, P(A|B)- Posterior probabilities.

If two values are independent of each other then,

P(A, B) =P(A) P(B)

Naïve Bayes can be build using the python library. Naïve’s predictors are independent, though they are used in recommendation systems. They are used in many real-time applications and well knowingly used in document classification.

Advantages:

Advantages are they require very less computational power, assumed in multiple class prediction problems, accurately work on large datasets.

Popular Course in this category
Machine Learning Training (17 Courses, 27+ Projects)17 Online Courses | 27 Hands-on Projects | 159+ Hours | Verifiable Certificate of Completion | Lifetime Access
4.7 (8,377 ratings)
Course Price

View Course

Related Courses
Deep Learning Training (15 Courses, 24+ Projects)Artificial Intelligence Training (3 Courses, 2 Project)

Disadvantage:

The main disadvantage of this classifier is they will assign zero probability. And they have features with are independent of each other.

2. Decision tree

It’s a top-down approach model with the structure of the flow-chart handles high dimensional data. The outcomes are predicted based on the given input variable. Decision tree composed of the following elements: A root, many nodes, branches, leaves. The root node does the partition based on the attribute value of the class, the internal node takes an attribute for further classification, branches make a decision rule to split the nodes into leaf nodes, lastly, the leaf nodes gives us the final outcome. The time complexity of the decision tree depends upon the number of records, attributes of the training data. If the decision tree is too long it is difficult to get the desired results.

Advantage: They are applied for predictive analytics to solve the problems and used in day to daily activities to choose the target based on decision analysis. Automatically builds a model based on the source data. Best in handling missing values.

Disadvantage: The size of the tree is uncontrollable until it has some stopping criteria. Due to their hierarchical structure tree is unstable.

3. Support Vector Machine

This algorithm plays a vital role in Classification problems and most popularly a machine learning supervised algorithms. It’s an important tool used by the researcher and data scientist. This SVM is very easy and its process is to find a hyperplane in an N-dimensional space data points. Hyperplanes are decision boundaries which classify the data points. All this vector falls closer to the hyperplane, maximize the margin of the classifier. If the margin is maximum, the lowest is the generalization error. Their implementation can be done with the kernel using python with some training datasets. The main target of the SVM is to train an object into a particular classification. SVM is not restricted to become a linear classifier. SVM is preferred more than any classification model due to their kernel function which improves computational efficiency.

Advantage: They are highly preferable for its less computational power and effective accuracy. Effective in high dimensional space, good memory efficiency.

Disadvantage: Limitations in speed, kernel, and size

4. Random Forest

It’s a powerful machine-learning algorithm based on the Ensemble learning approach. The basic building block of Random forest is the decision tree used to build predictive models. The work demonstration includes creating a forest of random decision trees and the pruning process is performed by setting a stopping splits to yield a better result. Random forest is implemented using a technique called bagging for decision making. This bagging prevents overfitting of data by reducing the bias similarly this random can achieve better accuracy. A final prediction is taken by an average of many decision trees i.e frequent predictions. The random forest includes many use cases like Stock market predictions, fraudulence detection, News predictions.

Advantages:

  • Doesn’t require any big processing to process the datasets and a very easy model to build. Provides greater accuracy helps in solving predictive problems.
  • Works well in handling missing values and automatically detects an outlier.

Disadvantage:

  • Requires high computational cost and high memory.
  • Requires much more time period.

5. K- Nearest Neighbors

Here we will discuss the K-NN algorithm with supervised learning for CART. They make use of K positive small integer; an object is assigned to the class based on the neighbors or we shall say assigning a group by observing in what group the neighbor lies. This is chosen by distance measure Euclidean distance and a brute force. The value of K can be found using the Tuning process. KNN doesn’t prefer to learn any model to train a new dataset and use normalization to rescale data.

Advantage: Produces effective results if the training data is huge.

Disadvantage: The biggest issue is that if the variable is small it works well. Secondly, choosing the K factor while classifying.

Conclusion

In conclusion, we have gone through the capabilities of different classification algorithms still acts as a powerful tool in feature engineering, image classification which plays a great resource for machine learning. Classification algorithms are powerful algorithms that solve hard problems.

Recommended Articles

This is a guide to Classification Algorithms. Here we discuss that the Classification can be performed on both structured and unstructured data with pros & cons. You can also go through our other suggested articles –

  1. Clustering Algorithm
  2. Data Mining Process
  3. Machine Learning Algorithms
  4. Most Used Techniques of Ensemble Learning
  5. C++ Algorithm | Examples of C++ Algorithm

Machine Learning Training (17 Courses, 27+ Projects)

17 Online Courses

27 Hands-on Projects

159+ Hours

Verifiable Certificate of Completion

Lifetime Access

Learn More

0 Shares
Share
Tweet
Share
Primary Sidebar
Machine Learning Tutorial
  • Algorithms
    • Machine Learning Algorithms
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • What is a Greedy Algorithm?
    • What is Genetic Algorithm?
    • Random Forest Algorithm
    • Nearest Neighbors Algorithm
    • Weak Law of Large Numbers
    • Ray Tracing Algorithm
    • SVM Algorithm
    • Naive Bayes Algorithm
    • Neural Network Algorithms
    • Boosting Algorithm
    • XGBoost Algorithm
    • Pattern Searching
    • Loss Functions in Machine Learning
    • Decision Tree in Machine Learning
    • Hyperparameter Machine Learning
    • Unsupervised Machine Learning
    • K- Means Clustering Algorithm
    • KNN Algorithm
    • Monty Hall Problem
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • What is Decision Tree?
    • What is Random Forest
  • Supervised
    • What is Supervised Learning
    • Supervised Machine Learning
    • Supervised Machine Learning Algorithms
    • Perceptron Learning Algorithm
    • Simple Linear Regression
    • Polynomial Regression
    • Multivariate Regression
    • Regression in Machine Learning
    • Hierarchical Clustering Analysis
    • Linear Regression Analysis
    • Support Vector Regression
    • Linear Regression Modeling
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is Regression Analysis?
    • Linear Regression Analysis
    • Clustering Methods
    • Backward Elimination
    • Ensemble Techniques
    • Bagging and Boosting
    • Linear Regression Modeling
    • What is Reinforcement Learning
  • Classification
    • Kernel Methods in Machine Learning
    • Clustering in Machine Learning
    • Machine Learning Architecture
    • Machine Learning C++ Library
    • Machine Learning Frameworks
    • Data Preprocessing in Machine Learning
    • Data Science Machine Learning
    • Classification of Neural Network
    • Neural Network Machine Learning
    • What is Convolutional Neural Network?
    • Single Layer Neural Network
    • Kernel Methods
    • Forward and Backward Chaining
    • Forward Chaining
    • Backward Chaining
  • Deep Learning
    • What Is Deep learning
    • Deep Learning
    • Application of Deep Learning
    • Careers in Deep Learnings
    • Deep Learning Frameworks
    • Deep Learning Model
    • Deep Learning Algorithms
    • Deep Learning Technique
    • Deep Learning Networks
    • Deep Learning Libraries
    • Deep Learning Toolbox
    • Types of Neural Networks
    • Convolutional Neural Networks
    • Create Decision Tree
    • Deep Learning for NLP
    • Caffe Deep Learning
    • Deep Learning with TensorFlow
  • RPA
    • What is RPA
    • What is Robotics?
    • Benefits of RPA
    • RPA Applications
    • Types of Robots
    • RPA Tools
    • Line Follower Robot
    • What is Blue Prism?
    • RPA vs BPM
  • Pytorch
    • PyTorch Versions
    • Single Layer Perceptron
    • PyTorch vs Keras
    • torch.nn Module
  • UiPath
    • What is UiPath
    • UiPath Careers
    • UiPath Architecture
    • UiPath Orchestrator
    • Uipath Reframework
    • UiPath Studio
  • Interview Questions
    • Machine Learning Interview Questions
    • Deep Learning Interview Questions And Answer
    • Machine Learning Cheat Sheet

Related Courses

Machine Learning Training

Deep Learning Training

Artificial Intelligence Training

Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

© 2020 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you
Book Your One Instructor : One Learner Free Class

Let’s Get Started

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA Login

Forgot Password?

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

Special Offer - Machine Learning Training (17 Courses, 27+ Projects) Learn More