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09:11
This Video is about Decision Tree Classification in Data Mining.
Views: 18414 Red Apple Tutorials

07:57
Creative Decision Tree Diagram in PowerPoint Follow this step by step screencast tutorial for a simple decision tree diagram to use in your next presentation. Key Links: ********** 25 Creative Ideas MINI Training http://www.presentation-process.com/25-creative-presentation-ideas-mini-training.html Comprehensive All In One Bundle - PowerPoint Templates: http://www.presentation-process.com/comprehensive-all-in-one-powerpoint-bundle.html Ramgopal's PowerPoint Mastery Training Program: http://www.presentation-process.com/ramgopals-powerpoint-mastery-program.html
Views: 4272 Presentation Process

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Views: 535704 MBAbullshitDotCom

09:26
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: 483488 Victor Lavrenko

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This video is about DECISION TREE ANALYSIS which will help you to understand the basic concept of decision tree analysis. In this video i have solved one practical question which will help you to get the process of solving any numerical question and example. After watching you will also get to know that how to construct the decision tree. I hope this will help you. Thanks JOLLY Coaching how to solve decision tree problem, Decision tree analysis, How to solve decision tree analysis, Practical solved questios on decision tree analysis. decision threoy decision tree analysis
Views: 100930 JOLLY Coaching

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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.

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the meaning of ID3_of Decision Tree Classification and example of it .
Views: 165 Ali Othman

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Classification in Data Mining with classification algorithms. Explanation on classification algorithm the decision tree technique with Example.

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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.

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Views: 59653 Ibrahim Almosallam

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In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-

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Views: 28241 Simplilearn

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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: 111792 Gopal Malakar

05:57
A tutorial about classification and prediction in Data Mining .
Views: 28303 Red Apple Tutorials

14:17
In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree

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Introduction Heart Diseases remain the biggest cause of deaths for the last two epochs. Recently computer technology develops software to assistance doctors in making decision of heart disease in the early stage. Diagnosing the heart disease mainly depends on clinical and obsessive data. Prediction system of Heart disease can assist medical experts for predicting heart disease current status based on the clinical data of various patients. In this project, the Heart disease prediction using classification algorithm Naive Bayes, and Random Forest is discussed. Naive Bayes Algorithm The Naive Bayes classification algorithm is a probabilistic classifier. It is based on probability models that incorporate strong independence assumptions. Naive Bayes is a simple technique for constructing classifiers models that assign class labels to problem instances. It assume that the value of a particular feature is independent of the value of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 10 cm in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of any possible correlations between the color, roundness, and diameter features. Random Forest Technique In this technique, a set of decision trees are grown and each tree votes for the most popular class, then the votes of different trees are integrated and a class is predicted for each sample. This approach is designed to increase the accuracy of the decision tree, more trees are produced to vote for class prediction. This approach is an ensemble classifier composed of some decision trees and the final result is the mean of individual trees results. Follow Us: Facebook : https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/ Twitter: https://twitter.com/e2matrix_lab/ LinkedIn: https://www.linkedin.com/in/e2matrix-thesis-jalandhar/ Instagram: https://www.instagram.com/e2matrixresearch/

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Views: 40030 edureka!

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In this video, you will learn how to solve a decision making problem using decision trees
Views: 37474 maxus knowledge

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Decision Tree Analysis is used to determine the expected value of a project in business. This video takes a step-by-step look at how to figure out the best optimized decision to use. SUBSCRIBE: https://goo.gl/atM3GS MORE VIDEOS: https://goo.gl/u1GBXN WEBSITE: https://goo.gl/LSfThB TWITTER: https://goo.gl/3Hlifb LINKEDIN: https://goo.gl/B42VPU GET OUR EBOOK: https://goo.gl/ogTxwJ
Views: 13694 Nick Harrison

14:57
DTREE is customised Excel file which helps to automate decision tree calculation and drawing in Excel.
Views: 2433 Santosh Singh

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In this video, I create a decision tree using Gini Impurity to determine the splitting attributes. I originally created this video (and the others in my series) to be used with a specific KDD class which is taught at my home university. I first encountered this algorithm in class there. If you would like to look into this topic in more detail, or read a bit about some similar algorithms, I am including the link to one of the presentations that I used as a reference. coitweb.uncc.edu/~ras/KBS-Class/1-Decision-Trees.ppt Thank you for watching!
Views: 45052 Laurel Powell

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Decision trees are powerful and surprisingly straightforward. Here's how they are grown. Code: https://github.com/brohrer/brohrer.github.io/blob/master/code/decision_tree.py Slides: https://docs.google.com/presentation/d/1fyGhGxdGcwt_eg-xjlMKiVxstLhw42XfGz3wftSzRjc/edit?usp=sharing PERMISSIONS: Original video was published with the Creative Commons Attribution license (reuse allowed). CREDITS: Original video source: https://www.youtube.com/watch?v=9w16p4QmkAI
Views: 7189 Coding Tech

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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .

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Views: 9163 freddy kresna

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Views: 356 Clickmyproject

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Data mining term project
Views: 72 parul singh

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Lecture Series on Artificial Intelligence by Prof. P. Dasgupta, Department of Computer Science & Engineering, IIT Kharagpur. For more Courses visit http://nptel.iitm.ac.in
Views: 115110 nptelhrd

09:51
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: 190579 Last moment tuitions

02:40
Micro-lesson on building decision tree models in MS Excel
Views: 1100 Dan Ryan

25:26
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: 4206 Analytics University

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Using the Excel add-in to create a very simple decision tree
Views: 15245 profMattDean

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Views: 180378 Augmented Startups

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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: 1828 Keshav Singh

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Naive Bayes is a machine learning algorithm for classification problems. It is based on Bayes’ probability theorem. It is primarily used for text classification which involves high dimensional training data sets. A few examples are spam filtration, sentimental analysis, and classifying news articles. It is not only known for its simplicity, but also for its effectiveness. It is fast to build models and make predictions with Naive Bayes algorithm. Naive Bayes is the first algorithm that should be considered for solving text classification problem. Hence, you should learn this algorithm thoroughly. This video will talk about below: 1. Machine Learning Classification 2. Naive Bayes Theorem About us: HackerEarth is building the largest hub of programmers to help them practice and improve their programming skills. At HackerEarth, programmers: 1. Solve problems on Algorithms, DS, ML etc(https://goo.gl/6G4NjT). 2. Participate in coding contests(https://goo.gl/plOmbn) 3. Participate in hackathons(https://goo.gl/btD3D2) Subscribe Our Channel For More Updates : https://goo.gl/suzeTB For More Updates, Please follow us on: Facebook : https://goo.gl/40iEqB Twitter : https://goo.gl/LcTAsM LinkedIn : https://goo.gl/iQCgJh Blog : https://goo.gl/9yOzvG
Views: 80396 HackerEarth

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In this example we will use SilverDecisions for creating a Decision Tree and evaluating it. Link: http://silverdecisions.pl
Views: 369 Khalid Khan

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Explaining business decision trees. Access our exam based, Business Studies course delivered in an environment that stops procrastination - https://studyrocket.co.uk/courses/business-a-level/ Study without procrastination. #business #alevel #ocr #aqa #studyrocket
Views: 133 Study Rocket

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Decision Tree for PowerPoint presentations. Get this graph at http://www.poweredtemplate.com/powerpoint-diagrams-charts/ppt-tree-diagrams/00040/0/index.html Download creative, pre-made, and complete editable diagrams, shapes, icons and charts at http://www.PoweredTemplate.com
Views: 298 PoweredTemplate.com

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IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2017): "Decision Stream: Cultivating Deep Decision Trees". PowerPoint presentation: https://www.slideshare.net/DmitryIgnatovPhD/decision-stream-cultivating-deep-decision-trees
Views: 26350 Dmitry Yu. Ignatov

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Data Science & Machine Learning - C5.0 Decision Tree Exercise - DIY- 27 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU C5.0 Decision Tree - Classification C50_model{anglebrace}- C5.0(train_Predictors, train_Target) C50_predict{anglebrace}- predict(C50_model, test_data) Get the data from Balance Scale Data Set. http://archive.ics.uci.edu/ml/datasets/Balance+Scale Citation Policy: If you publish material based on databases obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments. We suggest the following pseudo-APA reference format for referring to this repository: Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Here is a BiBTeX citation as well: @misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50 Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50 Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Intro - DIY- 25 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Use Case - DIY- 26 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Exercise - DIY- 27 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree,

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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.

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By Hang Yang.

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http://www.salford-systems.com/support This tutorial will help you improve your understanding of CART data mining models within the Salford Predictive Modeler.
Views: 889 Salford Systems

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The aim of this presentation is to show a brief description about the C4.5 algorithm, used to create Univariate Decision Trees. We also talk about Multivariate Decision Trees, their process to classify instances using more than one attribute per node in the tree. We try to discuss how they work, and how to implement the algorithms that build such trees, including examples of Univariate and Multivariate results.
Views: 51038 Thales Sehn Körting

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Views: 228 Arsen Grigoryan

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Part 2 of Decision Tree Analysis: Using Visio to create a Decision Tree and applying the technique to a sample problem.
Views: 43269 TheHollyVision

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This is additional material for Advanced Data Mining Class of WILP Students. It addresses pruning in GSP.
Views: 6643 Kamlesh Tiwari

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