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Clicked here http://www.MBAbullshit.com/ and OMG wow! I'm SHOCKED how easy.. No wonder others goin crazy sharing this??? Share it with your other friends too! Fun MBAbullshit.com is filled with easy quick video tutorial reviews on topics for MBA, BBA, and business college students on lots of topics from Finance or Financial Management, Quantitative Analysis, Managerial Economics, Strategic Management, Accounting, and many others. Cut through the bullshit to understand MBA!(Coming soon!) http://www.youtube.com/watch?v=a5yWr1hr6QY
Views: 527099 MBAbullshitDotCom

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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: 90920 JOLLY Coaching

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This brief video explains *the components of the decision tree *how to construct a decision tree *how to solve (fold back) a decision tree. Other videos: Decision Analysis 1: Maximax, Maximin, Minimax Regret https://youtu.be/NQ-mYn9fPag Decision Analysis 1.1 (Costs): Maximax, Maximin, Minimax Regret https://youtu.be/ajkXzvVegBk Decision Analysis 2.1: Equally Likely (Laplace) and Realism (Hurwicz) https://www.youtube.com/watch?v=zlblUq9Dd14 Decision Analysis 2: EMV & EVPI - Expected Value & Perfect Information https://www.youtube.com/watch?v=tbv9E9D2BRQ Decision Analysis 4: EVSI - Expected Value of Sample Information https://www.youtube.com/watch?v=FUY07dvaUuE Decision Analysis 5: Posterior Probability Calculations https://youtu.be/FpKiHpYnY_I
Views: 180611 Joshua Emmanuel

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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

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Views: 51447 SAS Software

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Lecture 4 Business Data Mining (Decision Tree) Part 1

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A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. We are going to use Weka. Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. You can download Weka from here

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http://www.salford-systems.com/products/cart/ Use SPM's Batteries ATOM and MINCHILD to control node size in decision trees.This tutorial will show you how and explain the importance of the automation features available. The Salford Predictive Modeler software suite is a highly-accurate data mining and predictive analytics software solution for a variety of industries. One of the core components of the SPM software suite is CART classification trees, and automation tools like ATOM and MINCHILD are widely used among sophisticated data mining experts worldwide.
Views: 108 Salford Systems

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Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUO Before breaking out the big algos on a new dataset, it is a good idea to first explore the simple, intuitive patterns (i.e. heuristics). This will pay off in droves. It not only exposes you to your data, it makes you understand it and gives you that critical 'business knowledge'. People you work with will ask you general questions about the data, and this is how you can get to it. In this post will explore how to find the important values that explain a particular target outcome. We'll use sklearn's DecisionTreeClassifier and graphviz for exporting and visualizing resulting trees. Blog/Code: http://amunategui.github.io/simple-heuristics/index.html Follow me on Twitter https://twitter.com/amunategui and signup to my newsletter: http://www.viralml.com/signup.html More on http://www.ViralML.com and https://amunategui.github.io Thanks!
Views: 3237 Manuel Amunategui

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This course focuses on data analytics and machine learning techniques in MATLAB using functionality within Statistics and Machine Learning Toolbox and Neural Network Toolbox. The course demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results. https://matlab4engineers.com/product/machine-learning/

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Views: 799 Catherine Fernando

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Data Science & Machine Learning - Apriori Hands-on Example - DIY- 37 -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 Hands On – R Machine Learning Ex-17 Get the Titanic: Machine Learning from Disaster data set from the following link, and predict survival on the Titanic passengers using Apriori Algorithm. https://www.kaggle.com/c/titanic/data 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 Data Science & Machine Learning - Random Forest Intro - DIY- 28 -of-50 Data Science & Machine Learning - Random Forest Hands on - DIY- 29 -of-50 Data Science & Machine Learning - Naive Bayes - DIY- 31 -of-50 Data Science & Machine Learning - Naive Bayes Handson- DIY- 32 -of-50 Data Science & Machine Learning - Naive Bayes Handson contd- DIY- 33 -of-50 Data Science & Machine Learning - Naive Bayes Exercise- DIY- 34 -of-50 Data Science & Machine Learning - Apriori Algorithm Concepts- DIY- 35 -of-50 Data Science & Machine Learning - Support Confidence Lift - Apriori- DIY- 36 -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, Random Forest, Naive Bayes, Apriori

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Views: 234 Allan Esser

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Get The MATLAB Course Bundle! https://josephdelgadillo.com/product/matlab-course-bundle/ Limited FREE course coupons available! https://goo.gl/VgXo4N Get the courses directly on Udemy! Go From Beginner to Pro with MATLAB! http://bit.ly/2v1e0lL Machine Learn Fundamentals with MATLAB! http://bit.ly/2v3sQs6 The Ultimate Guide for MATLAB App Development! http://bit.ly/2GOodDN MATLAB for Programming and Data Analysis! http://bit.ly/2IIwpWL This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The course consists of the following sections: Segment 1: Instructor and Course Introduction Segment 2: MATLAB Crash Course Segment 3: Grabbing and Importing Data-set Segment 4: K-Nearest Neighbor Segment 5: Naive Bayes Segment 6: Decision Trees Segment 7: Discriminant Analysis Segment 8: Support Vector Machines Segment 9: Error Correcting Output Codes Segment 10: Classification with Ensembles Segment 11: Validation Methods Segment 12: Evaluating Performance As bonus, you also learn how to share your analysis results with your colleges, friends, and others, and create visual analysis of your results. You will also have access to some practice questions which will give you hands on experience. Time Stamps: 01:29 Introduction 03:04 Why MATLAB for machine learning 06:16 Meet the instructor, Dr. Nouman Azam 09:25 MATLAB crash course 19:30 Applications of machine learning 31:28 Data types you will encounter 39:55 Importing data into MATLAB 49:36 Data tables Web - https://josephdelgadillo.com/ Subscribe - https://goo.gl/tkaGgy Follow for Updates - https://goo.gl/DPZvua

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Demonstration of the design and use of a decision tree structure for CEA. To view text subtitles for the audio portion, click the CC button on the bottom right of the video viewer.

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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: 30521 MIT OpenCourseWare

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Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 91301 Siraj Raval

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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: 4469 Nitin Paighowal

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For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of association analysis, which includes topics such as market basket analysis and product recommendation engines. The first practical example centers around analyzing supermarket retailer product receipts and the second example touches upon the use of the association rules in the political arena.
Views: 30924 Derek Kane

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https://www.salford-systems.com/products/spm Data mining train and test data consistency in CART Classification and Regression Trees software within the Salford Predictive Modeler software suite.
Views: 737 Salford Systems

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CIMA P1 Decision Trees Free lectures for the CIMA P1 Exams
Views: 2375 OpenTuition

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In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees.
Views: 291698 Thales Sehn Körting

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http://www.salford-systems.com/ Improve your understanding of variable importance in CART classification and regression trees. Classification and regression trees (CART) is one of Salford Systems' flagship data mining products and is a core component of the Salford Predictive Modeler software suite. CART is an optimal tool for segmentation, targeted marketing, fraud detection and a variety of other applications. Decision tree software is widely used among commercial and academic institutions, and CART is one of the leading tools used for predictive modeling and data mining worldwide.
Views: 1862 Salford Systems

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Speaker: Weimin Wang Synopsis: A binary classification problem (products recommendation) using PySpark on hadoop platform is presented. Specifically, presentation using ipython notebook will go through details such as - 1) data pre-processing, 2) Using mllib random forest classifier for binary classification, 3) Measuring performance using AUC score, 4) Different strategies to handle the problem of unbalanced dataset Speaker: Weimin Wang - works as Data Scientist in Merck Singapore. During his job, he focuses on Advanced Analytics and Bioinformatics Research. With solid knowledge in Data Mining and Machine Learning. Weimin is also actively involved in Data Science competitions like Kaggle and Data Science Game. His interests lie in Machine Learning, Deep Learning and Natural Language Processing. Event Page: https://www.meetup.com/PyData-SG/events/229711656/ Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/ZoKQ/
Views: 4301 Engineers.SG

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Classification Trees are part of the CART family of technique for prediction. Here we use the package rpart, with its CART algorithms, in R to learn a classification tree model on the 'iris' data set available in all R installations. In this video I also compare our results from rpart to our results from C5.0 in the previous classification tree tutorial video called "

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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-454308909/m-473338565 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 6671 Udacity

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Views: 20857 Prabhudev Konana

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Just-in-Time Videos - Management: Introduction to Decision Trees Presented and Prepared by Charlene Chu in Collaboration with Maria Wesslén
Views: 14167 UTM MCS Math Videos

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visit our website for full course www.lastmomenttuitions.com here we have discussed the filtering technique which is collaborative filtering Simply "Here we need to make a recommendation system which will recommend user a particular product based on the choices of other user's." NOTES: https://lastmomenttuitions.com/how-to-buy-notes/ bda notes form : https://goo.gl/Ti9CQj introduction to Hadoop : https://goo.gl/LCHC7Q Introduction to Hadoop part 2 : https://goo.gl/jSSxu2 Distance Measures : https://goo.gl/1NL3qF Euclidean Distance : https://goo.gl/6C16RJ Jaccard distance : https://goo.gl/C6vmWR Cosine Distance : https://goo.gl/Sm48Ny Edit Distance : https://goo.gl/dG3jAP Hamming Distance : https://goo.gl/KNw95L FM Flajolit martin Algorithm : https://goo.gl/ybjX9V Random Sampling Algorithm : https://goo.gl/YW1AWh PCY ( park chen yu) algorithm : https://goo.gl/HVWs21 Collaborative Filtering : https://goo.gl/GBQ7JW Bloom Filter Basic concept : https://goo.gl/uHjX5B Naive Bayes Classifier : https://goo.gl/dbRYYh Naive Bayes Classifier part2 : https://goo.gl/LWstNv Decision Tree : https://goo.gl/5m8JhA Apriori Algorithm :https://goo.gl/mmpxL6 FP TREE Algorithm : https://goo.gl/S29yV8 Agglomerative clustering algorithmn : https://goo.gl/L9nGu8 Hubs and Authority and Hits Algorithm : https://goo.gl/D2EdFG Betweenness Centrality : https://goo.gl/czZZJR
Views: 5718 Last moment tuitions

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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]itions.com 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: 178231 Last moment tuitions

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Introducing advanced analytics in RapidMiner through a product demonstration of RapidMiner Studio Professional.
Views: 19376 RapidMiner, Inc.

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The challenge: a Kaggle competition to correctly label two million StackOverflow posts with the labels a human would assign. The tools: scikit-learn, 16GB of RAM, and a massive amount of data. The goal: place above 50% in a Kaggle competition against data scientists from around the world from the comfort of my laptop. The talk: lessons learned from going deep with scikit-learn for tackling a very tricky machine learning problem and dealing with a lot of strange text and many labels. Explore the wonders of tf-idf, multi-label SGD classification, the power of n-grams and developing intuition around feature design, along with spinoff applicability to other work Cerner is doing. About the Speaker: Chris Finn is a Senior Principal Architect and Distinguished Engineer in Cerner's Medical Informatics group. Since joining Cerner in 1991, he has worked on a number of R&D efforts at Cerner including semantic search, community e-prescribing, and most recently, research into machine learning topics involving textual analysis aimed at improving documentation quality. In addition to R&D responsibilities, Chris contributes to a variety of talent development and outreach programs, including contributing curriculum to the new Project Lead the Way computer science course being piloted across the country during the 2013-14 school year, as well as building out a DevArc Academy course on the topic of modeling and simulation. This talk was given at DevCon, Cerner's internal engineering conference. Check us out at http://engineering.cerner.com/ Cerner DevCon 2014 June 3, 2014
Views: 28560 CernerEng

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RapidMiner classification tutorial for CS2401 by Team Wobbles. Jay Yeo Ng Yan Xiang Magnus Pang Dionne Lee Theresia Marten Downloads: https://my.rapidminer.com/nexus/account/index.html#downloads Compare versions: https://rapidminer.com/products/comparison/ German Credit Data Set: http://www.learnpredictiveanalytics.com/uploads/4/2/1/5/42154413/pa_dm_files_dec_15_2014.zip
Views: 6099 Jay Yeo

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Logistic Regression - Fun and Easy Machine Learning https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for classification problems. Despite the name “logistic regression” this is not an algorithm for regression Logistic Regression is a little bit similar to Linear Regression in the sense that both have the goal of estimating the values for the parameters/coefficients, so the at the end of the training of the machine learning model we got a function that best describe the relationship between the known input and the output values... 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: 50731 Augmented Startups

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Views: 3367 FrontlineSolvers

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This video introduces the concept of regression trees as recursive, piece-wise constant fits in order to identify an underlying response surface. It then walks through an example of a CART model setup using the Boston housing data and discusses the results. Finally, a summary of how this approach simplifies the underlying structure by constructing piece-wise constant models, by segmenting the underlying population into a set of mutually exclusive smaller segments, such that within each segment the overall prediction is a constant and as you go from segment to segment, the prediction changes by a fixed amount. http://www.salford-systems.com
Views: 656 Salford Systems

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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 30582 Last Minute Tutorials

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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: 505 KDD2017 video

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Sara Robertson, VP, Product Engineering, Xaxis
Views: 871 AppNexus

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Views: 48224 Data Science Dojo

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Views: 21704 SAS Software

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Views: 35 wenyi an

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Risk and Uncertainty - Decision Trees Part 2 - ACCA Performance Management (PM) *** Complete list of free ACCA lectures is available on OpenTuition.com https://opentuition.com/acca/pm/ *** Free lectures for the ACCA Performance Management (PM) Exam To benefit from this lecture, visit opentuition.com/acca to download the notes used in the lecture and access ALL free resources: ACCA lectures, tests and Ask the ACCA Tutor Forums Please go to opentuition to post questions to ACCA Tutor, we do not provide support on youtube.
Views: 1203 OpenTuition

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