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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: 144965 Well Academy
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 150742 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: 456642 Victor Lavrenko
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: 11515 Pallab Sanyal
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 Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. 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: 52780 edureka!
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
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: 19105 Simplilearn
Random Forest - Fun and Easy Machine Learning
 
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Random Forest - Fun and Easy Machine Learning https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML 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. To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)
Views: 154145 Augmented Startups
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] Call us at US: +18336900808 (Toll Free) or India: +918861301699 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 24743 edureka!
Basic Data Mining - Lession 4 - Decision Tree Model
 
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Exploring the Targeted Mailing Models (Basic Data Mining Tutorial) - Exploring the Decision Tree Model
Views: 80 Anh Lee
Decision Tree (CART) - Machine Learning Fun and Easy
 
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Decision Tree (CART) - Machine Learning Fun and Easy 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. To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :) -------------------------------------------------- Support us on Patreon http://bit.ly/PatreonArduinoStartups --------------------------------------------------
Views: 94435 Augmented Startups
Decision Tree
 
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In this video, I show you how a Decision Tree works.
Views: 12745 zooce
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: 45936 Bharatendra Rai
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) and OMG wow! I'm SHOCKED how easy it was .. No wonder others going crazy sharing this??? Share it with your other friends too! Code for visualising a decision tree - https://github.com/bhattbhavesh91/visualize_decision_tree Please Subscribe! That is the thing you could do that would make me happiest. You can find me on: GitHub - https://github.com/bhattbhavesh91 Medium - https://medium.com/@bhattbhavesh91
Views: 13429 Bhavesh Bhatt
Data Mining - Decision Tree
 
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Use a view to make predictions about bike purchases.
Views: 10950 Mike
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: 6945 Big Edu
Predicting Customer Churn Decision Tree Model
 
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In this video you will learn the how to build a Decision Tree to understand data that is driving customer churn using RapidMiner. Learn more at www.cdoadvisors.com
Views: 3547 CDO Advisors
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.
Classification Trees (CART) Data Mining
 
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Statgraphics 18 contains a procedure for implementing CART (Classification and Regression Trees). CART is a machine learning method for making predictions from data. This video describes a classification model for predicting wine classifications. To review more machine learning options and procedures, visit http://www.statgraphics.com/data-mining.
Views: 1517 Statgraphics
Technical Course: Decision Trees: Decision Tree Analysis
 
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Decision Tree Tutorial and Introduction by Jigsaw Academy. This is part one of the Decision Tree tutorial from our Foundation Analytics course (http://www.jigsawacademy.com/online-analytics-training). In this example, we look at how decision trees can be used by credit card companies to market themselves to a target audience of potentially profitable customers. Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training. Follow us on: https://www.facebook.com/jigsawacademy https://twitter.com/jigsawacademy http://jigsawacademy.com/
Views: 80368 Jigsaw Academy
CART Regression Trees Algorithm - Excel part 1
 
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CART, Classification and Regression Trees is a family of Supervised Machine Learning Algorithms. Follow this link for an entire Intro course on Machine Learning using R, did I mention it's FREE: https://www.youtube.com/playlist?list=PLjPbBibKHH18I0mDb_H4uP3egypHIsvMn Also, be sure to check out my channel for over 400 tutorials on Excel, R, Statistics, Machine Learning, basic Math, and more.
Views: 8415 Jalayer Academy
Introduction to Ensemble Learning, Bagging and Boosting
 
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Click here for in depth study with quiz / workout - https://www.udemy.com/decision-tree-theory-application-and-modeling-using-r/?couponCode=YOU_DT_0
Views: 10974 Gopal Malakar
Decision Tree Induction (in Hindi)
 
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This Video is about Decision Tree Classification in Data Mining.
Views: 13639 Red Apple Tutorials
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: 1631 Keshav Singh
SSAS - Data Mining - Decision Trees, Clustering, Neural networks
 
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SSAS - Data Mining - Decision Trees, Clustering, Neural networks
Views: 1010 M R Dhandhukia
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: 95554 Victor Lavrenko
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: 65693 WekaMOOC
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. 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/
Data Mining with RapidMiner - DecisionTree (Thai)
 
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เฉลย Quiz Data Mining วิชา 703413
Views: 358 Damrongsak Naparat
Gini Index | Decision Tree - Part 1 (Hindi - English)
 
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This video is the simplest hindi english explanation of gini index in decision tree induction for attribute selection measure.
Views: 17432 Red Apple Tutorials
Constructing Classification and Regression Tree (CART) Using IBM SPSS Modeler
 
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In this tutorial, I will show you how to construct and Classification and Regression Tree (CART) for data mining purposes. We show through example of bank loan application dataset. We then will show steps to explore and interpret the constructed tree. I hope that help and let me know if you have any questions. Thanks.
Views: 15785 IT_CHANNEL
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: 68212 Melvin L
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: 1069 Learning With Mahamud
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: 7383 Big Edu
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: 19671 Wes Doyle
CART-Classification and Regression Trees
 
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Gini Index in CART Entropy Pruning CART Cost Complexity Cost Complexity Pruning Classification and Regression Trees Pruning
Views: 9686 Sunil Bhatia
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: 4680 Jayanth Kurup
(ML 2.1) Classification trees (CART)
 
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Basic intro to decision trees for classification using the CART approach. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA
Views: 100340 mathematicalmonk
Data Mining Lecture Bangla -- Decision Tree।Solved Example Part 1
 
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A decision tree is a structure that includes a root node, branches, and leaf nodes. Each internal node denotes a test on an attribute, each branch denotes the outcome of a test, and each leaf node holds a class label. The topmost node in the tree is the root node. Email : [email protected]
Views: 1039 Asaduzzaman Kanok
What is Random Forest Algorithm? A graphical tutorial on how Random Forest algorithm works?
 
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It Explains Random Forest Method in a very simple and pictorial way --------------------------------- Read in great detail along with Excel output, computation and R code ---------------------------------- https://www.udemy.com/decision-tree-theory-application-and-modeling-using-r/?couponCode=Ad_Try_01
Views: 108353 Gopal Malakar
Decision Tree in R
 
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Here I will describe about what is decision tree,how to implement decision tree model in R,how to plot roc curve in decision tree in R,implement decision tree using rpart,calculate auc in R,decision tree using rpart
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: 4336 Nitin Paighowal
Extremely Fast Decision Tree Mining for Evolving Data Streams
 
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Extremely Fast Decision Tree Mining for Evolving Data Streams Albert Bifet (Telecom ParisTech) Jiajin Zhang (Noah's Ark Lab, Huawei) Wei Fan (Huawei Noah’s Ark Lab) Cheng He (Noah's Ark Lab, Huawei) Jianfeng Zhang (Noah's Ark Lab, Huawei) Jianfeng Qian (Huawei Noah's Ark Lab) Geoffrey Holmes (University of Waikato) Bernhard Pfahringer (University of Waikato) Nowadays real-time industrial applications are generating a huge amount of data continuously every day. To process these large data streams, we need fast and efficient methodologies and systems. A useful feature desired for data scientists and analysts is to have easy to visualize and understand machine learning models. Decision trees are preferred in many real-time applications for this reason, and also, because combined in an ensemble, they are one of the most powerful methods in machine learning. In this paper, we present a new system called streamDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei. Streaming decision trees adapt to changes on streams, a huge advantage since standard decision trees are built using a snapshot of data, and can not evolve over time. streamDM-C++ is easy to extend, and contains more powerful ensemble methods, and a more efficient and easy to use adaptive decision tree. We compare our new implementation with VFML, the current state of the art implementation in C, and show how our new system outperforms VFML in speed using less resources. More on http://www.kdd.org/kdd2017/
Views: 484 KDD2017 video
Decision Tree Model
 
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Building a Decision Tree Model with SQL Server Analysis Services
Views: 898 Chuc Nguyen Van
Regression with Decision Trees
 
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My web page: www.imperial.ac.uk/people/n.sadawi
Views: 20673 Noureddin Sadawi
link spam detection using decision trees in orange (data mining)
 
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in this video i will be discussing about link spam detection using decision tree classifier
Views: 245 Anshuman Singh

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