EDUCBA

EDUCBA

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

Decision Making Techniques

By Priya PedamkarPriya Pedamkar

Home » Data Science » Data Science Tutorials » Machine Learning Tutorial » Decision Making Techniques

decision making techniques

Introduction to Decision Making Techniques

  • In these articles, we will learn about Decision Making Techniques. AI/ML methods can possibly be a means to an end for our Rational Decision-Making. This article is intended to provide intuition on the most popular decision-making techniques steered by AI/ML.
  • Right from waking up and leaving behind the cozy bed in the morning until we retire for the day, the decisions we make are what keep us thriving. It is estimated that an average human makes around 35,000 decisions a day. Even a small decision can have a massive consequence on a set of events like stated in the very famous “butterfly effect”, where a minor change such as a butterfly flapping its wings can create a phenomenal change.
  • The key to effective decision-making is to determine the possible outcomes, evaluate them to get to the best one. With the rise of AI, we are tending towards it for most of our rational decisions, be it finding the shortest route to reach work or making complex business choices. The use of AI/ML techniques for decision making allows us to find the optimum solution by trying out various possible outcomes for the problem at hand.

Top Decision-Making Techniques Using AI/ML

Decision Making is a continuous and goal-oriented process. It involves a series of actions to be performed to reach a defined target. The following techniques give solutions to modern business problems, thereby enhancing the decision-making capabilities to provide effective, cost-effective and profitable growth in any given area.

1. Recommendation Systems

Let’s take a moment to analyze our daily activities. Most of our day begins and continue to progress with our smartphones. From the news, we read to things we shop, and the watch is all recommended to us based on our past choices. In a way, the machines are making decisions on what we might be interested in. This is achieved by building a system that uses techniques as illustrated below,

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Decision Making Techniques 1

2. Predictive Analytics

Predictive models are the ones that use statistics-based techniques to estimate or predict the possible outcomes given proper valid data that fills the requirements for various scenarios. A predictive model is often combined with Machine Learning algorithms to obtain an effective business outcome. This is achieved by data mining, determining the trends and patterns w.r.t the target, and applying a suitable algorithm of Classification (to predict various classes) or Regression (to predict numbers).

The use of Predictive Analysis in various industries are shown below:

Decision Making Techniques 2

3. Decision Making Based on Fuzzy Logic

The term “Fuzzy” means vague or imprecise. Many decisions we make cannot be treated as black or white; sometimes, it is necessary to explore the outcomes the grey region offers. Fuzzy logic is used to integrate a resemblance of human reasoning in machines. Fuzzy logic is often implemented with control systems to provide an acceptable real-time outcome. When teamed up with Neural networks and reinforcement techniques, fuzzy systems can be used to achieve the intellect and stability required for real-world scenarios.

Fuzzy systems have increased performance and are less sensitive towards external noise and outliers. These systems are more flexible and can contain values that represent more than two possible situations. Though fuzzy systems are not always accurate, they are more robust and find their applications in most of the household products like air conditioning, microwave ovens, washing machine, refrigerators, television and so on.

Importance of Decision Making

Below are the important points to explain Decision Making Techniques:

  • It is already a known fact that each day we generate more than 2.5 quintillion bytes of data which is only rising each passing day. So, it is safe to conclude that there is no shortage of data for the “data-driven” companies. However, since most of these data are unstructured, a need arises for them to be mined, cleaned and cleansed in order to be able to extract useful information from the said data.
  • The success of any business lies within the route they take to reach out to their customers and how pleased the customers are with their intended products. The use of AI and ML techniques is important for the business to understand their market and to keep the right foot forward towards innovation and effective use of the available resources.
  • These techniques act as a bridge to obtain leverage over the data and make use of them for complex decision making, enabling the business to have a deeper, personal understanding of their customers, resulting in a stronger bond between them and better business opportunities to explore.

Advantages of Decision Making

Below are the advantages of Decision Making

1. Exploring More Options

With lots of data comes lots of possibilities to explore and extract useful insights from them. While this task can be tedious to humans, machines can help us achieve this. On the path to accomplishing an ideal decision, data is analyzed, studied for multiple alternatives to find solutions to the unresolved questions.

2. Time-Saving

With the growth of the digital platform, it is the need of the moment to get quick results, and this is possible with the involvement of efficient and trained machines that applies complex mathematics rules to give us the best output. With the advancements in neural networks and supercomputers, these complex algorithms are now performed in a matter of seconds to hours rather than days altogether.

3. More Accuracy and Efficiency

In addition to saving our time, AI and ML systems provide us with accurate results for our problems. This is made possible by the loads of machine acceptable data we feed into these systems, and as the time progresses with the historic data accumulated, the decisions interpreted get better and better.

4. Understanding Consumers

Retaining the consumers is as important as a task as obtaining them. The business can make use of the data they get on their consumers and can work around their existing approach and decide what’s best for both in order to be sure that their consumers are not going away any time soon.

Popular Course in this category
Sale
Machine Learning Training (19 Courses, 29+ Projects)19 Online Courses | 29 Hands-on Projects | 178+ Hours | Verifiable Certificate of Completion | Lifetime Access
4.7 (13,314 ratings)
Course Price

View Course

Related Courses
Deep Learning Training (15 Courses, 24+ Projects)Artificial Intelligence Training (5 Courses, 2 Project)

Conclusion

Decision-Making Techniques, Decision making provides us with a glimpse of various possible alternatives so that we can make the choice of that one best decision. Lately, we have leaned on to AI to make most of the decisions for us, to help us make more precise and improved decisions. Though this is crucial for us to progress into the future, not all decisions made by machines and algorithms can replace the decisions that humans make.

The morality that is present in the human decisions is what the machine-based decisions mostly lack, not to mention the bias that they are introduced by their creator or by the data they are fed. But the scope of AI involving in our decision process is not going to be slowed down any time soon; the only way for us to evolve further is to adopt a more smart and calculative approach to improve on the decisions that are the outcomes of our actions.

Recommended Articles

This is a guide to Decision Making Techniques. Here we discuss Top Decision-Making Techniques Using AI/ML, importance with its advantages. You can also go through our other related articles to learn more –

  1. Deep Learning Technique
  2. Decision Tree in Data Mining
  3. Career Making Decisions
  4. How Artificial Intelligence Works?

All in One Data Science Bundle (360+ Courses, 50+ projects)

360+ Online Courses

50+ projects

1500+ Hours

Verifiable Certificates

Lifetime Access

Learn More

2 Shares
Share
Tweet
Share
Primary Sidebar
Machine Learning Tutorial
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Naive Bayes in Machine Learning
    • Dataset Labelling
    • DataSet Example
    • Dataset ZFS
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • What is Kernel in Machine Learning
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Top 7 Useful Benefits Of Machine Learning Certifications
    • Machine Learning Python vs R
    • Optimization for Machine Learning
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Support Vector Machine in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • Dataset for Linear Regression
    • Decision tree limitations
    • What is Decision Tree?
    • What is Random Forest
  • Algorithms
    • Machine Learning Algorithms
    • Apriori Algorithm in Machine Learning
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • Hierarchical Clustering Agglomerative
    • 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
  • 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
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is 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
    • Automation Anywhere 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
    • Overviews 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 Tensors
    • What is PyTorch?
    • PyTorch MSELoss()
    • PyTorch NLLLOSS
    • PyTorch MaxPool2d
    • PyTorch Pretrained Models
    • PyTorch Squeeze
    • PyTorch Reinforcement Learning
    • PyTorch zero_grad
    • PyTorch norm
    • PyTorch VAE
    • PyTorch Early Stopping
    • PyTorch requires_grad
    • PyTorch MNIST
    • PyTorch Conv2d
    • Dataset Pytorch
    • PyTorch tanh
    • PyTorch bmm
    • PyTorch profiler
    • PyTorch unsqueeze
    • PyTorch adam
    • PyTorch backward
    • PyTorch concatenate
    • PyTorch Embedding
    • PyTorch Tensor to NumPy
    • PyTorch Normalize
    • PyTorch ReLU
    • PyTorch Autograd
    • PyTorch Transpose
    • PyTorch Object Detection
    • PyTorch Autoencoder
    • PyTorch Loss
    • PyTorch repeat
    • PyTorch gather
    • PyTorch sequential
    • PyTorch U-NET
    • PyTorch Sigmoid
    • PyTorch Neural Network
    • PyTorch Quantization
    • PyTorch Ignite
    • PyTorch Versions
    • PyTorch TensorBoard
    • PyTorch Dropout
    • PyTorch Model
    • PyTorch optimizer
    • PyTorch ResNet
    • PyTorch CNN
    • PyTorch Detach
    • Single Layer Perceptron
    • PyTorch vs Keras
    • torch.nn Module
  • UiPath
    • What is UiPath
    • UiPath Action Center
    • UiPath?Orchestrator
    • UiPath web automation
    • UiPath Orchestrator API
    • UiPath Delay
    • UiPath Careers
    • UiPath Architecture
    • UiPath version
    • Uipath Reframework
    • UiPath Studio
  • 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
  • Live Classes
  • 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

© 2022 - 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

By signing up, you agree to our Terms of Use and Privacy Policy.

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

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

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

By signing up, you agree to our Terms of Use and Privacy Policy.

Special Offer - Machine Learning Training Learn More