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Data Mining Lecture -- Decision Tree | Solved Example (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 211533 Well Academy
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 294203 Last moment tuitions
Decision Tree 1: how it works
 
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Full lecture: http://bit.ly/D-Tree A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Each split corresponds to a node in the. Splitting stops when every subset is pure (all elements belong to a single class) -- this can always be achieved, unless there are duplicate training examples with different classes.
Views: 534344 Victor Lavrenko
شجرة القرارات - Decision Trees
 
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Download slides from here: https://drive.google.com/file/d/0BwkBn0oFDraSX2hIRTVVWXlnQlE/view?usp=sharing
Views: 66095 Ibrahim Almosallam
Decision Tree Classification Algorithm – Solved Numerical Question 1 in Hindi
 
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Decision Tree Classification Algorithm – Solved Numerical Question 1 in Hindi Data Warehouse and Data Mining Lectures in Hindi
DM11 How does Decision Tree Work
 
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أ.محمود رفيق الفرا مختصر مساق التنقيب عن البيانات Data Mining
Views: 2611 MahmoudRFarra
Decision Tree with R | Complete Example
 
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Also called Classification and Regression Trees (CART) or just trees. R file: https://goo.gl/Kx4EsU Data file: https://goo.gl/gAQTx4 Includes, - Illustrates the process using cardiotocographic data - Decision tree and interpretation with party package - Decision tree and interpretation with rpart package - Plot with rpart.plot - Prediction for validation dataset based on model build using training dataset - Calculation of misclassification error Decision trees are an important tool for developing classification or predictive analytics models related to analyzing big data or data science. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 59200 Bharatendra Rai
Data Mining - Decision tree
 
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Decision tree represents decisions and decision Making. Root Node,Internal Node,Branch Node and leaf Node are the Parts of Decision tree Decision tree is also called Classification tree. Examples & Advantages for decision tree is explained. Data mining,text Mining,information Extraction,Machine Learning and Pattern Recognition are the fileds were decision tree is used. ID3,c4.5,CART,CHAID, MARS are some of the decision tree algorithms. when Decision tree is used for classification task, it is also called classification tree.
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Data Science |Simplilearn
 
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This Decision Tree algorithm in Machine Learning tutorial video will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms. Below topics are covered in this Decision Tree Algorithm Tutorial: 1. What is Machine Learning? ( 02:25 ) 2. Types of Machine Learning? ( 03:27 ) 3. Problems in Machine Learning ( 04:43 ) 4. What is Decision Tree? ( 06:29 ) 5. What are the problems a Decision Tree Solves? ( 07:11 ) 6. Advantages of Decision Tree ( 07:54 ) 7. How does Decision Tree Work? ( 10:55 ) 8. Use Case - Loan Repayment Prediction ( 14:32 ) What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 51567 Simplilearn
Decision Tree : Theory of Decision Tree |CHAID|CART| Data Science
 
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Decision Tree is a classification algorithm used to classify data in to groups. There are also good classification algorithms that works exactly like decision tree such as: Decision Tree based Methods Rule-based Methods Memory based reasoning Neural Networks Naïve Bayes and Bayesian Belief Networks Support Vector Machines Logistic Regression ANalytics Study Pack : https://analyticuniversity.com Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 8946 Big Edu
Decision Tree (CART) - Machine Learning Fun and Easy
 
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Decision Tree (CART) - Machine Learning Fun and Easy ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression (CART). So a decision tree is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 157524 Augmented Startups
Data Mining - Decision Tree
 
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Use a view to make predictions about bike purchases.
Views: 11671 Mike
Data Mining | Decision Tree | Decision Tree Analysis | Decision Tree Example
 
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Data Mining | Decision Tree | Decision Tree Analysis | Decision Tree Example *********************************************** data mining, decision tree algorithm, decision tree learning algorithm, decision tree algorithm example, decision tree algorithm in data mining, decision tree algorithm in r, decision tree, Decision Tree Basic, decision tree algorithm, decision tree analysis, decision tree machine learning, decision tree learning, decision tree learning example, decision tree entropy, decision tree regression, decision making process, decision making, decision tree examples, decision tree maker, decision tree classifier, decision tree python, Please Subscribe My Channel
Views: 1927 Learning With Mahamud
Decision Tree Important Points ll Machine Learning ll DMW ll Data Analytics ll Explained in Hindi
 
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Decision Tree Explained with Example https://youtu.be/RVuy1ezN_qA 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 40678 5 Minutes Engineering
Decision Tree Classification in R
 
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This video covers how you can can use rpart library in R to build decision trees for classification. The video provides a brief overview of decision tree and the shows a demo of using rpart to create decision tree models, visualise it and predict using the decision tree model
Views: 80830 Melvin L
SSAS - Data Mining - Decision Trees, Clustering, Neural networks
 
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SSAS - Data Mining - Decision Trees, Clustering, Neural networks
Views: 1466 M R Dhandhukia
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
 
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** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training ** This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial: 1. What is Classification? 2. Types of Classification 3. Classification Use Case 4. What is Decision Tree? 5. Decision Tree Terminology 6. Visualizing a Decision Tree 7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm Subscribe to our channel to get video updates. Hit the subscribe button above. Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm #decisiontree #decisiontreepython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 76900 edureka!
Machine Learning with Python - Part 2: Decision Tree
 
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In this series, we'll explore machine learning with Python by building a classifier to determine whether or not we might like a song based on its attributes, which are provided by the Spotify API. We'll use an existing data set from Kaggle to explore and implement various classifiers. In Part 2, we'll create a Decision Tree classifier and visualize it using graphviz, pydotplus, scipy, and matplotlib! I'll speak briefly about the advantages and disadvantages of Decision Tree classifiers. If you enjoy my videos, support me on Patreon! https://www.patreon.com/wesdoyle Throughout this series, we'll: - Perform Exploratory Data Analysis (EDA) in a Jupyter Notebook using Pandas, Numpy, matplotlib, and other commonly-used libraries - Build a Decision Tree classifier using scikit-learn - Build a Random Forest classifier using scikit-learn - Build an Artificial Neural Network classifier using Keras Links: Link to the dataset on Kaggle: https://www.kaggle.com/geomack/spotifyclassification Intro track is adapted from "despondency" by Fog Lake, used with permission from the artist. Go check out his music, it's fantastic! - https://foglake.bandcamp.com/
Views: 29064 Wes Doyle
Decision Tree Induction (in Hindi)
 
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This Video is about Decision Tree Classification in Data Mining.
Views: 25561 Red Apple Tutorials
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machine Learning |Simplilearn
 
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This Decision Tree in R tutorial video will help you understand what is decision tree, what problems can be solved using decision trees, how does a decision tree work and you will also see a use case implementation in which we do survival prediction using R. Decision tree is one of the most popular Machine Learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables. In simple words, a decision tree is a tree-shaped algorithm used to determine a course of action. Each branch of the tree represents a possible decision, occurrence or reaction. Now let us get started and understand how does Decision tree work. Below topics are explained in this Decision tree in R tutorial : 1. What is Decision tree? 2. What problems can be solved using Decision Trees? 3. How does a Decision Tree work? 4. Use case: Survival prediction in R Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/WsM21R Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Decision-Tree-in-R-HmEPCEXn-ZM&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 6592 Simplilearn
Decision Tree Algorithm Explained With Example ll DMW ll ML Easiest Explanation Ever in Hindi
 
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Decision Tree Algorithm Part 2 https://youtu.be/ffZ0ShPi-wg 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚 Decision Tree Algorithm DMW Data Mining And Warehousing Information Gain Entropy formula Gain Formula Decision Tree Solved Example
Views: 50598 5 Minutes Engineering
5. Building Decision Tree Models using RapidMiner Studio
 
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This video describes (1) how to build a decision tree model, (2) how to interpret a decision tree, and (3) how to evaluate the model using a classification matrix.
Views: 19245 Pallab Sanyal
Decision Tree Classification Algorithm – Solved Numerical Question 2 in Hindi
 
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Decision Tree Classification Algorithm – Solved Numerical Question 2 in Hindi Data Warehouse and Data Mining Lectures in Hindi
Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka Decision Tree tutorial will help you understand all the basics of Decision tree. This decision tree tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn decision tree analysis along with examples. Below are the topics covered in this tutorial: 1) Machine Learning Introduction 2) Classification 3) Types of classifiers 4) Decision tree 5) How does Decision tree work? 6) Demo in R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #decisiontree #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 66269 edureka!
Decision Tree -Data mining in Visual studio 2013
 
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How to create Decision Tree in Visual studio 2013
Views: 343 Nex Console
Decision Tree 5: overfitting and pruning
 
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Full lecture: http://bit.ly/D-Tree A decision tree can always classify the training data perfectly (unless there are duplicate examples with different class labels). In the process of doing this, the tree might over-fit to the peculiarities of the training data, and will not do well on the future data (test set). We avoid overfitting by pruning the decision tree.
Views: 112948 Victor Lavrenko
Random Forest - Fun and Easy Machine Learning
 
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Random Forest - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ Hey Guys, and welcome to another Fun and Easy Machine Learning Algorithm on Random Forests. Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. In general, the more trees in the forest the more robust the prediction. In the same way in the random forest classifier, the higher the number of trees in the forest gives the high accuracy results. To model multiple decision trees to create the forest you are not going to use the same method of constructing the decision with information gain or gini index approach, amongst other algorithms. If you are not aware of the concepts of decision tree classifier, Please check out my lecture here on Decision Tree CART for Machine learning. You will need to know how the decision tree classifier works before you can learn the working nature of the random forest algorithm. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 238682 Augmented Startups
Bagging & Boosting Algorithms | Decision Tree | Data Science
 
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In this video you will learn about theory behind bootstrap method of building decision tree and combining them for better prediction.. This type of algorithms are known as Bagging & boosting or in general known as Ensemble learning . Apart from these two random forest is also a popular ensemble training algorithms ANalytics Study Pack : https://analyticuniversity.com Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 10847 Big Edu
Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8
 
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Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
Views: 230084 Google Developers
Decision Tree Learning using ID3 Algorithm | Artificial intelligence | Machine Learning
 
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#askfaizan | #syedfaizanahmad | #decisiontree PlayList : Artificial Intelligence : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH Bayesian Network in Artificial Intelligence | Bayesian Belief Network | https://youtu.be/0U5xH4b7nPc Decision Tree Learning using ID3 Algorithm | Artificial intelligence https://youtu.be/pvTejBgiF3I Supervised Learning and Unsupervised Learning | Learning in Artificial Intelligence https://youtu.be/Wn2JgBfAsSM Genetic Algorithm | Artificial Intelligence Tutorial in Hindi Urdu https://youtu.be/frB2zIpOOBk Comparison of Search Algorithm https://youtu.be/QMz7jwXDvwg Resolution in Artificial Intelligence | Resolution Rules in AI https://youtu.be/oQmqJPLqHZA Inference rules in Predicate logic https://youtu.be/Y8KCh4VRRwM Predicate logic in AI | First order logic in Artificial Intelligence https://youtu.be/sFINpc5KA3E Wumpus World Proving | Propositional logic Example https://youtu.be/bDu9iNJ8h58 PROPOSITIONAL LOGIC | Artificial Intelligence https://youtu.be/oUR11UUIDvA Knowledge based Agents | Logical agents https://youtu.be/Y7CS-1BfA6o Alpha Beta Pruning | Problem #2 https://youtu.be/QL-g1FDls74 A Decision tree represents a function that takes as input a vector of attribute values and returns a “decision”—a single output value. The input and output values can be discrete or continuous. A decision tree reaches its decision by performing a sequence of tests. There are many specific decision-tree algorithms. Notable ones include: ID3 (Iterative Dichotomiser 3) C4.5 (successor of ID3) CART (Classification And Regression Tree) CHAID (Chi-squared Automatic Interaction Detector). Performs multi-level splits when computing classification trees. MARS: extends decision trees to handle numerical data better. ID3 is one of the most common decision tree algorithm Dichotomisation means dividing into two completely opposite things. Algorithm iteratively divides attributes into two groups which are the most dominant attribute and others to construct a tree. Then, it calculates the Entropy and Information Gains of each attribute. In this way, the most dominant attribute can be founded. After then, the most dominant one is put on the tree as decision node. Entropy and Gain scores would be calculated again among the other attributes. Procedure continues until reaching a decision for that branch. algorithm steps: Calculate the entropy of every attribute using the data set S Entropy(S) = ∑ – p(I) . log2p(I) Split the set S into subsets using the attribute for which the resulting entropy (after splitting) is minimum (or, equivalently, information gain is maximum) Gain(S, A) = Entropy(S) – ∑ [ p(S|A) . Entropy(S|A) ] Make a decision tree node containing that attribute Recurse on subsets using remaining attributes. for Complete Artificial Intelligence Videos click on the link : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/askfaizan1/ Instagram page : https://www.instagram.com/ask_faizan/ Twitter : https://twitter.com/ask_faizan/
Views: 45827 Ask Faizan
Predicting Target variable Values in Decision Tree Using Weka
 
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Hi Everyone, We usually create a data model but we restrict ourselves till the model creation but we actually don't predict the future values. This video is about how you can predict the target variable values in decision tree. I have used Weka for this implementation. The data set i have used is "Vote" dataset which comes along with Weka. I create 2 data sets - one was Training data set without last 30 rows, and Test data set with last 30 rows but no values for target variable. You can create test data set with "?" implanted for target values in test set.
Views: 5110 Nitin Paighowal
MS SQL Server Data mining- decision tree
 
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A quick example on how to do data mining using decision tree algorithm within MS SQL Server . We analyze patterns in data that is heavily skewed for specific cases so that we can validate the model.
Views: 5689 Jayanth Kurup
Prediction and Classification with Decision Tree
 
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This vlog introduces you to decision tree in R and how categorical data can be classified and predicted by this algorithm.
Views: 2295 Keshav Singh
StatQuest: Decision Trees
 
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This StatQuest focuses on the machine learning topic "Decision Trees". Decision trees are a simple way to convert a table of data that you have sitting around your desk into a means to predict and classify new data as it comes. There is a minor error at 12:43: The Gini Impurity for Chest Pain should be 0.19. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest
Gini index based Decision Tree
 
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How does a Decision Tree Work? A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Splitting stops when every subset is pure (all elements belong to a single class) Code for visualising a decision tree - https://github.com/bhattbhavesh91/visualize_decision_tree If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: GitHub - https://github.com/bhattbhavesh91 Medium - https://medium.com/@bhattbhavesh91 #decisiontree #Gini #machinelearning #python #giniindex
Views: 32111 Bhavesh Bhatt
Regression Trees (Machine Learning/Data Mining)
 
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Statgraphics 18 contains a procedure for building Classification and Regression Trees, a machine learning method for making predictions from data. This video describes a regression model for predicting automobile miles per gallon. To review more machine learning options and procedures, visit http://www.statgraphics.com/data-mining.
Views: 135 Statgraphics
Decision Tree Algorithm ll Constructing of Decision Tree ll ML ll DMW Explained in Hindi
 
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Decision Tree Algorithm Part 1 https://youtu.be/RVuy1ezN_qA 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚 Decision Tree Algorithm DMW Data Mining And Warehousing Information Gain Entropy formula Gain Formula Decision Tree Solved Example
Views: 29243 5 Minutes Engineering
Decision Tree - Pruning and Pruning Parameters | Part-10
 
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The problem of over fitting can be addressed using pruning method. The process of adjusting decision tree to minimize classification error is pruning. Using a sample data set in the lab exercise, the method of pruning to overcome the problem of over fitting is explained in detail. Watch the video for more information! Data Scientists take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics, and programming to clean, massage and organize. But worry not we are here to the rescue and teach you how to be a data scientist, more importantly, upgrade your analytic skills to tackle any problem in the field of data science. Join us on "statinfer.com" for becoming a "scientist in data science" Our "Machine Learning" course is now available on Udemy https://www.udemy.com/machine-learning-made-easy-beginner-to-advance-using-r/ Part 1 – Introduction to R Programming. This is the part where you will learn basic of R programming and familiarize yourself with R environment. Be able to import, export, explore, clean and prepare the data for advance modeling. Understand the underlying statistics of data and how to report/document the insights. Part 2 – Machine Learning using R Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees. Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it. Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means. Data science YouTube playlist. https://www.youtube.com/statinferanalytics Facebook link:- (Visit our facebook page we are sharing data science videos) https://www.facebook.com/aboutanalytics/
Views: 4732 Statinfer Analytics
Decision Tree Solved | Id3 Algorithm (concept and numerical) | Machine Learning (2019)
 
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Decision Tree is a supervised learning method used for classification and regression. It is a tree which helps us by assisting us in decision-making! Decision tree builds classification or regression models in the form of a tree structure. It breaks down a data set into smaller and smaller subsets and simultaneously decision tree is incrementally developed. The final tree is a tree with decision nodes and leaf nodes. A decision node has two or more branches. Leaf node represents a classification or decision. We cannot do more split on leaf nodes. The topmost decision node in a tree which corresponds to the best predictor called root node. Decision trees can handle both categorical and numerical data. #codewrestling #decisiontree #machinelearning #id3 Common terms used with Decision trees: Root Node: It represents entire population or sample and this further gets divided into two or more homogeneous sets. Splitting: It is a process of dividing a node into two or more sub-nodes. Decision Node: When a sub-node splits into further sub-nodes, then it is called decision node. Leaf/ Terminal Node: Nodes do not split is called Leaf or Terminal node. Pruning: When we remove sub-nodes of a decision node, this process is called pruning. You can say opposite process of splitting. Branch / Sub-Tree: A sub section of entire tree is called branch or sub-tree. Parent and Child Node: A node, which is divided into sub-nodes is called parent node of sub-nodes whereas sub-nodes are the child of parent node. How does Decision Tree works ? Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables. Advantages of Decision Tree: 1. Easy to Understand: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Its graphical representation is very intuitive and users can easily relate their hypothesis. 2. Useful in Data exploration: Decision tree is one of the fastest way to identify most significant variables and relation between two or more variables. With the help of decision trees, we can create new variables / features that has better power to predict target variable. It can also be used in data exploration stage. For e.g., we are working on a problem where we have information available in hundreds of variables, there decision tree will help to identify most significant variable. 3 Decision trees implicitly perform variable screening or feature selection. 4. Decision trees require relatively little effort from users for data preparation. 5. Less data cleaning required: It requires less data cleaning compared to some other modeling techniques. It is not influenced by outliers and missing values to a fair degree. 6. Data type is not a constraint: It can handle both numerical and categorical variables. Can also handle multi-output problems. ID3 Algorithm Key Factors: Entropy- It is the measure of randomness or ‘impurity’ in the dataset. Information Gain: It is the measure of decrease in entropy after the dataset is split. Ask me A Question: [email protected] Music: https://www.bensound.com For Decision Trees slides comment below 😀
Views: 1822 Code Wrestling
Data Mining with Weka (3.4: Decision trees)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Decision trees http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 73024 WekaMOOC
Decision Tree for Classification Problems | data Science
 
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In this video you will learn about building a decision tree models for classification problem. Decision tree is a supervised learning algorithm. It can be used to classify data into categories. It is similar to other classification algorithms such as Logistic Regression, Multi nominal Logistic regression, Random forest, Support vector Machine , deep learning. Contact : [email protected] ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 4549 Analytics University
13. Classification
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 43970 MIT OpenCourseWare
Data Mining | C 4.5/ Decision Tree Algorithm - in Arabic
 
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Here I answer the question, How to construct a decision tree? by calculating the attributes Information Gain. The slides are available at: https://drive.google.com/open?id=0B6GKQ-raZgKiSUdxdDlvUnBpbUU
Views: 2185 Alaa Khaled