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Search results “Data mining classification rules and the tariff”
Weka Tutorial 09: Feature Selection with Wrapper (Data Dimensionality)
 
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This tutorial shows you how you can use Weka Explorer to select the features from your feature vector for classification task (Wrapper method)
Views: 66008 Rushdi Shams
Weka Tutorial 18: Classification 101 with Knowledge Flow Environment (Classification)
 
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This tutorial shows the introduction with the Weka knowledge flow environment
Views: 24782 Rushdi Shams
Making sense of the confusion matrix
 
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How do you interpret a confusion matrix? How can it help you to evaluate your machine learning model? What rates can you calculate from a confusion matrix, and what do they actually mean? In this video, I'll start by explaining how to interpret a confusion matrix for a binary classifier: 0:49 What is a confusion matrix? 2:14 An example confusion matrix 5:13 Basic terminology Then, I'll walk through the calculations for some common rates: 11:20 Accuracy 11:56 Misclassification Rate / Error Rate 13:20 True Positive Rate / Sensitivity / Recall 14:19 False Positive Rate 14:54 True Negative Rate / Specificity 15:58 Precision Finally, I'll conclude with more advanced topics: 19:10 How to calculate precision and recall for multi-class problems 24:17 How to analyze a 10-class confusion matrix 28:26 How to choose the right evaluation metric for your problem 31:31 Why accuracy is often a misleading metric == RELATED RESOURCES == My confusion matrix blog post: https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/ Evaluating a classifier with scikit-learn (video): https://www.youtube.com/watch?v=85dtiMz9tSo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=9 ROC curves and AUC explained (video): https://www.youtube.com/watch?v=OAl6eAyP-yo == DATA SCHOOL INSIDERS == Join "Data School Insiders" on Patreon for bonus content: https://www.patreon.com/dataschool == WANT TO GET BETTER AT MACHINE LEARNING? == 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 4) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 7109 Data School
Weka Tutorial 25: Sparse Data (Data Preprocessing)
 
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In this tutorial I demonstrate the way to represent sparse data in ARFF file format that Weka can read. Link in: http://www.linkedin.com/pub/rushdi-shams/3b/83b/9b3
Views: 8319 Rushdi Shams
Weka Tutorial 08: Numeric Transform (Data Preprocessing)
 
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Weka provides a filter called NumericTransform so that you can use the Java.Lang.Math class methods to transform your feature values. This is particularly useful as for some classification algorithms you will see that they perform better with integer values than real numbers or vice versa.
Views: 30774 Rushdi Shams
Weka Tutorial 23: Classification 101 using API (Classification)
 
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This tutorial shows how to train a classifier on data using the Java API
Views: 16693 Rushdi Shams
Data Mining with Weka (2.2: Training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 71755 WekaMOOC
Data Mining & Business Intelligence| ML | Tutorial #36 | Boosting (Adaboost)
 
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Pay me by using my PayPal.Me page: https://paypal.me/ranjiraj currency rates as per your country standards Order my books at 👉 http://www.tek97.com/ #Boosting #AdaptiveBoosting #Adaboost Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj This video will discuss regarding the algorithm and the idea of what is actually the Boosting technique in Data Mining is. Watch Now! سوف يناقش هذا الفيديو فيما يتعلق بالخوارزمية وفكرة ما هو في الواقع تقنية Boosting في Data Mining. شاهد الآن ! Este video discutirá sobre el algoritmo y la idea de lo que es realmente la técnica Boosting en Data Mining. Ver ahora ! In diesem Video wird der Algorithmus und die Idee der Boost-Technik im Data Mining diskutiert. Schau jetzt ! В этом видео будет обсуждаться алгоритм и идея того, что на самом деле является технологией Boosting в Data Mining. Смотри ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 456 Ranji Raj
Interpreting Results and Accuracy in Weka
 
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What those summary results mean, including precision, recall, f-measures, ROC AUC, and the confusion matrix.
Views: 4401 jengolbeck
Data Mining with Weka (2.6: Cross-validation results)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 6: Cross-validation results http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 28937 WekaMOOC
Weka Tutorial 06: Discretization (Data Preprocessing)
 
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An important feature of Weka is Discretization where you group your feature values into a defined set of interval values. Experiments showed that algorithms like Naive Bayes works well with discretized feature values
Views: 57599 Rushdi Shams
Data Mining with Weka (2.3: Repeated training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 3: Repeated training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 44133 WekaMOOC
28 Predictive Analytics Training with Weka (Class 4 Questions)
 
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Data Mining and Predictive Analytics training course using the open source Weka tool. Videos producted by the University of Waikato, New Zealand. Posted by Rapid Progress Marketing and Modeling, LLC (RPM2) under CC BY 3.0 RPM2 is a full-service Predictive Analytics and Data Sciences Services company specializing in Model Development, Consulting, Direct Marketing Services, and Professional Training. Visit us at http://www.RPMSquared.com/
Views: 1465 Predictive Analytics
Data Mining with Weka (3.2: Overfitting)
 
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Data Mining with Weka: online course from the University of Waikato Class X - Lesson X: Overfitting 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: 26560 WekaMOOC
Weka Tutorial 05: Held-out Testing (Classification)
 
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Weka machine learning tool has the option to develop a classifier and apply that to your test sets. This tutorial shows you how.
Views: 53267 Rushdi Shams
More Data Mining with Weka (4.1: Attribute selection using the "wrapper" method)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 1: Attribute selection using the "wrapper" method http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 15162 WekaMOOC
PPW Conference - Double Conversion Rates: Replace Rule-based systems with a Machine Learning
 
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By https://datarevenue.com/ Machine learning is helping ImmobilienScout24 increase revenues from its marketing emails. The team at Data Revenue have worked with IS24 to make this happen. This presentation looks at how machine learning is helping companies better target customers. Filmed at Property Portal Watch, Barcelona 2016. Data Revenue is the umbrella company for: https://scoring.ai/ - User Lifecycle Scoring for Real Estate Portals, and http://mvp.ai/ - Building AIs for data driven businesses Give us a ring: Markus Schmitt +49 160 9911 2824 Mon - Fri, 9:00-22:00 Find us at the office: Data Revenue, Rheinsberger Straße 76 10115 Berlin, Germany
Views: 1011 Data Revenue
More Data Mining with Weka (1.3: Comparing classifiers)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 3: Comparing classifiers http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 16274 WekaMOOC
Advanced Data Mining with Weka (2.6: Application to Bioinformatics – Signal peptide prediction)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 6: Application to Bioinformatics – Signal peptide prediction http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2847 WekaMOOC
Visualizing Probabilistic Classification Data in WEKA
 
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Data Visualization tool aims to visualize classifiers results in order to analyze classification performance, find sources of classification errors, and test possible improvements to the classification algorithm.
Data Mining for Causal Inference
 
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As an increasing amount of daily activity---ranging from what we purchase to who we talk---shifts to online platforms, it is only natural to ask how those platforms impact our behavior. Take, for instance, online recommendation systems: how much activity do recommendations actually cause over and above what would have happened in their absence? Without doing randomized experiments, which may be costly or infeasible, estimating the impact of such systems is non-trivial. In this talk, I will argue that careful data mining can help in answering relevant causal questions in a more general way than traditional observational approaches. In the first example, I will show how data mining can be used to augment a popular technique, instrumental variables, by searching for large and sudden shocks in time series data. Applying this method to system logs for Amazon's "People who bought this also bought" recommendations, we are able to analyze over 4,000 unique products that experience such shocks. This leads to a more accurate estimate of the impact of the recommender system: at least 75% of recommendation click-throughs would likely occur in their absence, questioning popular industry estimates based on observed click-through rates. In the second example, I will present a general data-driven identification strategy for finding natural experiments in time series data, inspired from the shock-based approach above. This method too reveals a similar overestimate for the impact of recommendation systems. See more on this video at https://www.microsoft.com/en-us/research/video/data-mining-causal-inference/
Views: 1468 Microsoft Research
Weka Tutorial 35: Creating Training, Validation and Test Sets (Data Preprocessing)
 
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The tutorial that demonstrates how to create training, test and cross validation sets from a given dataset.
Views: 74947 Rushdi Shams
Data Mining with Weka (2.5: Cross-validation)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Cross-validation http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 39016 WekaMOOC
Data Mining with Weka (1.4: Building a classifier)
 
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Data Mining with Weka (1.4: Building a classifier) Data Mining with Weka Data Mining weka
More Data Mining with Weka (4.5: Counting the cost)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 5: Counting the cost http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 6257 WekaMOOC
Classification Trees in R
 
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A conceptual introduction to classification trees, bagging, and random forests using R. Download the R syntax and data file at this URL: https://www.dropbox.com/s/1rkqxp0188fquou/CART.YouTube.SyntaxData.zip?dl=0
Views: 5067 Terry Jorgensen
About Medical Data Mining | Medical Data Mining L01T04 | Introduction & Scientific Knowledge
 
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The Online Certificate Program in Genomics and Biomedical Informatics Bar-Ilan University & Sheba Medical Center Course 803.80-675 - Medical Data Mining Spring 2018 Lecturer: Dr. Ronen Tal-Botzer [email protected] Unit L01: Introduction & Scientific Knowledge Topic T04: About Medical Data Mining
14. Tests for Binary & Categorical Data
 
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Basic Statistics: Simple Group Comparisons With Dr Helen Brown, Senior Statistician at The Roslin Institute, December 2015 *Recommended Youtube playback settings for the best viewing experience: 1080p HD ************************************************ Content: Tests for binary or categorical data Simple tests to compare groups Chi-squared test : --Based on comparing expected frequencies to observed frequencies --Most suitable for larger datasets --Popular test Fisher’s exact test : --Always suitable even if some frequencies are very small or zero Both tests may be used to compare 2 groups or equality of 3+ groups As before null hypothesis is ‘no difference between groups’ Example: Binary data -Do success rates of treatments for treating a disease in animals differ? But are there differences between pairs of treatments in success rates? Pairwise comparisons of each pair of treatments
Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 11692 Microsoft Research
Using Data Mining & Analytics to Proactively Manage Emergency Room ER) Super Utilizers
 
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Presenter: Ze Jiang iQuartic, PHMC, and clinical partners have been developing data technologies and clinical operational models to identify existing super-utilizers, identify effective methods for direct intervention with super-utilizers, and that use behavior modeling to pro-actively identify potential future super-utilizers In this workshop, iQuartic intends to provide an overview of the process, and highlight the successes as well as areas of ongoing development. Using data to pro-actively manage ER super-utilizers requires a coordinated approach among clinical partners, the data technology developers, and even affected payers. Information sharing systems to allow for longitudinal tracking of patients may also be necessary. An effective data driven approach to control ER super-utilization does not need to be expensive or complicated. In fact, a well-planned and clinician-centric approach can produce successes. iQuartic would like to share some of its planning and operational insights on this topic, and explore with workshop participants if this approach may be appropriate for them.
Views: 502 NAMGT
Class Project
 
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Class Project for Introduction to Data Mining.
Views: 288 Eric Frohnhoefer
Model Building and Testing Instead of Statistical Significance
 
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In most of the last half of the 20th century the goal of quantitative research in the humanities and very often in other areas as well, such as medicine, was to obtain statistically significant results. This is no longer the goal of quantitative research in the social sciences. Although many researchers and statisticians have suggested greater emphasis on effect sizes and statistics like confidence intervals, we need a more radical change in how we understand what the goals of research are. In this video we propose that researchers adopt the view that research is fundamentally about model building and model testing. This concept is used in data mining and also the great Nobel prize winning physicist Richard Feynman described research in physics as making guesses and then carefully testing the guess. In modern parlance we can call this building models and testing models. Although this paradigm for understanding the goals of research is simple and may sound very general, there are specific guidelines that are important for conducting research properly within this paradigm. In this video 10 guidelines are given for properly conducting research and reporting results. In some academic fields some of these guidelines are already being violated as researchers leave the old paradigm of seeking statistical significance to an alternative paradigm. Ten guidelnes for best practices for quantitative research in the social sciences are: 1. Be clear about what stage your research is in from initial guesses to confirmation of a theory in a hypothesis test. Do not exaggerate the stage of the research. 2. State clearly whether you are building a model with training data or testing a model with testing data. 3. Data Mining: A set of predictor variables that is not consistent, i.e., contains unrelated predictors is unlikely to be validated with test data! Data mining expert Sam Roweis emphasized this. 4. Data Mining: A set of predictors with no basis in theory is “a shot in the dark”. If the effect sizes found are small, then this is very much a shot in the dark. 5. The benefits of qualitative research: Some researchers see a strict divide between qualitiative research and quantitative research. However, very often it is qualitative research that develops content expertise that provides the best basis for what Richard Feynman called the guess. 6. Extraordinary proposed relationships require extraordinary support. 7. Strive to remove selection bias from your data! This might be the most important guideline for best practices! 8. Make friends with your data and use data visualization. 9. Pay close attention to effect sizes, confidence intervals, measurement error, rates of false positives and false negatives, power of the statistical test, etc. Use p-values as a guide. P-values have become very intensely debated in recent years. Virtually everyone acknowledges that they have been abused. Swinging to opposite extreme by declaring them verboten is ill-advised. 10. Early exploratory research is now welcome. Research into areas that have little research literature, and therefore the literature review section of a paper might be almost non-existent, would be heretical in the old paradigm. Hopefully this video provides a clearer understanding of what the goals of research in the social sciences are in the 21st century, and how we can conduct research and report results in a responsible manner. Note also that in this video only the transition from a search for statistical significance to a model-building-and-testing philosophical framework are considered. These approaches are part of the essentially positivist tradition in research. Other philosophical frameworks based on other traditions are important but are not the focus of interest in this video. The lecturer, David Cochrane, conducts research in the fringe area of astrology and his guidelines are important for researchers in astrology as well as education, psychology, other social sciences, and other fields.
Lecture 4: Word Window Classification and Neural Networks
 
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Lecture 4 introduces single and multilayer neural networks, and how they can be used for classification purposes. Key phrases: Neural networks. Forward computation. Backward propagation. Neuron Units. Max-margin Loss. Gradient checks. Xavier parameter initialization. Learning rates. Adagrad. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
Weka Tutorial 36: Learning Curve 1 (Model Evaluation)
 
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This video demonstrates how to produce learning curves in Weka. Learning curves can be produced in two common ways: (1) By varying data (2) By varying model parameters. This tutorial shows how to produce learning curves for a classifier by varying the amount of data.
Views: 14164 Rushdi Shams
31 Predictive Analytics Training with Weka  (Data mining and ethics)
 
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Data Mining and Predictive Analytics training course using the open source Weka tool. Videos producted by the University of Waikato, New Zealand. Posted by Rapid Progress Marketing and Modeling, LLC (RPM2) under CC BY 3.0 RPM2 is a full-service Predictive Analytics and Data Sciences Services company specializing in Model Development, Consulting, Direct Marketing Services, and Professional Training. Visit us at http://www.RPMSquared.com/
Views: 1764 Predictive Analytics
Can I Machine Learn it? #1: The problem with Rule-based Segmentation
 
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We aim to answer all of your Machine Learning questions and queries. Get in touch if you have a specific question about Machine Learning you would like us to cover! Email: [email protected] In this episode we discuss a common conversation we have with large companies who thing they are already getting the most from segmenting. Of course segmenting is a very good step and greatly improves conversion rates. However, there is a similarly great improvement possible by leveraging machine learning for targeting. We discuss why. Data Revenue is the umbrella company for: https://scoring.ai/ - User Lifecycle Scoring for Real Estate Portals, and http://mvp.ai/ - Building AIs for data driven businesses
Views: 3024 Data Revenue
Identifying Suspicious URLs: An Application of Large-Scale Online Learning
 
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Google Tech Talk May 5, 2010 ABSTRACT Presented by Justin Ma. We explore online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. We show that this application is particularly appropriate for online algorithms as the size of the training data is larger than can be efficiently processed in batch and because the distribution of features that typify malicious URLs is changing continuously. Using a real-time system we developed for gathering URL features, combined with a real-time source of labeled URLs from a large Web mail provider, we demonstrate that recently-developed online algorithms can be as accurate as batch techniques, achieving daily classification accuracies up to 99% over a balanced data set. Slides: http://cseweb.ucsd.edu/~jtma/google_talk/jtma-google10.pdf Justin Ma is a PhD candidate at UC San Diego advised by Stefan Savage, Geoff Voelker and Lawrence Saul. His research interests are in systems and networking with an emphasis on network security, and his current focus is the application of machine learning to problems in security. He will be joining UC Berkeley as a postdoc after graduation. [Home page: http://www.cs.ucsd.edu/~jtma/ ]
Views: 10249 GoogleTechTalks
More Data Mining with Weka (5.2: Multilayer Perceptrons)
 
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More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 2: Multilayer Perceptrons http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/rDuMqu https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 29712 WekaMOOC
Weka Tutorial 24: Model Comparison (Model Evaluation)
 
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In this tutorial, you will learn how to use Weka Experimenter to compare the performances of multiple classifiers on single or multiple datasets. Please subscribe to get more updates and like if the tutorial is useful. Link in: http://www.linkedin.com/pub/rushdi-shams/3b/83b/9b3
Views: 28525 Rushdi Shams
Tally.ERP 9 in Hindi ( Meaning,Classification, Rules of Accounting ) Part 5
 
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Tally ERP 9 Basic Level - https://goo.gl/6x1SYM इस विडियो में आप जानेगें एकाउंटिंग के नियमों के बारे में In this video you will know about Meaning,Classification, Rules of Accounting .
Views: 458590 Gyanyagya
McCullough: Data Is Wrecking Macro Tourists
 
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In case you missed it, the “macro tourists” have been out in force in recent weeks. They’re all over the place… popping up like unwanted weeds on CNBC, Bloomberg, Zero Hedge…the list is endless. These unfortunates have been loudly (and blindly) pointing to various political news stories to predict the end of the bull market. The data continues to stymie their myopic market calls. On the heels of a very positive US jobs report, which propelled the Nasdaq to all-time highs Friday, Hedgeye CEO Keith McCullough reminded investors in the clip above from The Macro Show to remain data-dependent. “Now you get a wonderful selling opportunity in all the things that we’ve liked now that the entire world knows the economy is accelerating,” McCullough says. “Volatility is not your friend from an equity perspective. You are in a friendly space from a sector return perspective.” Sure, it may seem like there are a lot of things to freak out about (Trump tariffs.. Gary Cohn.. Stormy Daniels..). We would just like to remind you don’t be a tourist. There’s not much worse (or less profitable) than being a macro tourist. Stick with the process. Be data dependent. Watch the full clip above for more.
Views: 494 Hedgeye
weka preprocessing
 
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Views: 676 Kalyan Chandra
Weka Tutorial 30: Multiple ROC Curves (Model Evaluation)
 
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ROC curves produced from different classifiers are a good means to compare classifier performances. This session demonstrates the use of Knowledge-flow environment of Weka to generate multiple ROC curves for more than one classifiers. Tutorial 28 shows how to generate a single ROC curve for a single classifier using Weka Explorer. The tutorial can be found at http://www.youtube.com/watch?v=j97h_-b0gvw
Views: 22962 Rushdi Shams

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