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Search results “Naive bayes classification algorithm in data mining”

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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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Naive Bayes Classification Algorithm – Solved Numerical Question 1 in Hindi Data Warehouse and Data Mining Lectures in Hindi

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Naive Bayes Classifier- Fun and Easy Machine Learning https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence. So lets take a look deeper at the formula, • We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence. • Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here. • And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence. So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes. 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: 83778 Augmented Startups

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naive Bayes classifiers in data mining or machine learning are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s,and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis. for more refer to https://en.wikipedia.org/wiki/Naive_Bayes_classifier naive bayes classifier example for play-tennis Download PDF of the sum on below link https://britsol.blogspot.in/2017/11/naive-bayes-classifier-example-pdf.html *****************************************************NOTE********************************************************************************* The steps explained in this video is correct but please don't refer the given sum from the book mentioned in this video coz the solution for this problem might be wrong due to printing mistake. **************************************************************************************************************************************** All data mining algorithm videos Data mining algorithms Playlist: http://www.youtube.com/playlist?list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr ******************************************************************** book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar *********************************************
Views: 38736 fun 2 code

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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: 64685 HackerEarth

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Naive Bayes Classification Algorithm – Solved Numerical Question 2 in Hindi Data Warehouse and Data Mining Lectures in Hindi

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Introduction to Bayesian theory and Bayes classification with an easy example.
Views: 32071 Saurabh Singh

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Simple example of the Naive Bayes classification algorithm
Views: 121952 Francisco Iacobelli

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Views: 245 Ranji Raj

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This tutorial starts with introduction of Dataset. All aspects of dataset are discussed. Then basic working of RapidMiner is discussed. Once the viewer is acquainted with the knowledge of dataset and basic working of RapidMiner, following operations are performed on the dataset. K-NN Classification Naïve Bayes Classification Decision Tree Association Rules
Views: 35097 RapidMinerTutorial

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Provides steps for applying Naive Bayes Classification with R. Data: https://goo.gl/nCFX1x R file: https://goo.gl/Feo5mT Machine Learning videos: https://goo.gl/WHHqWP Naive Bayes Classification is an important tool related to analyzing big data or working in data science field. 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: 13197 Bharatendra Rai

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#Naive_Bayes #Bayesian_Algorithm #Machine_Learning, #Classification_Technique #R_Studio This is an elementary level video in which we learn to use the Bayesian Algorithm for classification. Ideally Bayesian Algorithm is appropriate in case of two levels of classification, but we have tried to use it on IRIS dataset which has 3 levels of classification. We have also used it on Breast Cancer data file from #Kaggle. You can find the Breast Cancer dataset from the link provided below. Stay tuned for more advanced level videos on Bayesian Algorithm. https://www.dropbox.com/s/2qkskdmv7nywv7p/Breast_Cancer.csv?dl=0
Views: 153 Rajesh Dorbala

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Views: 9725 edureka!

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Views: 39683 edureka!

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This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.
Views: 86372 Francisco Iacobelli

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Here are some of the most commonly used classification algorithms -- Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest and Support Vector Machine. https://analyticsindiamag.com/7-types-classification-algorithms/ -------------------------------------------------- Get in touch with us: Website: www.analyticsindiamag.com Contact: [email protected] Facebook: https://www.facebook.com/AnalyticsIndiaMagazine/ Twitter: http://www.twitter.com/analyticsindiam Linkedin: https://www.linkedin.com/company-beta/10283931/ Instagram: https://www.instagram.com/analyticsindiamagazine/

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Views: 11118 GKMC datamining

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أهلا وسهلا فيكم بالدرس الثاني من سلسلة شروحات خوارزميات التنقيب عن البيانات - خوارزمية الـ Naive Bayes .. بتمنى يكون الشرح واضح وإذا في عندكم أي تساؤل جاهزين إنشالله. . ماتنسوا تشاركوا السلسلة مع زملائكم وتدعموا القناة لنستمر بالعطاء ^^ . Facebook: https://goo.gl/wdVMHs
Views: 860 Knowledge Network

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Views: 19202 Simplilearn

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Views: 14726 TheEngineeringWorld

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simple and easy explanation of Naive Bayes Algorithm in Hindi
Views: 6901 Red Apple Tutorials

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Views: 1121 Mahmudul Hasan

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Data Warehouse and Mining For more: http://www.anuradhabhatia.com

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An introduction to "naive Bayes" classifiers, in which we model the features as conditionally independent given the class.
Views: 63037 mathematicalmonk

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How to apply naive bayes algorithm | classifier in weka tool ? In this video, I explained that how can you apply naive bayes algorithm in weka tool.
Views: 4182 DataMining Tutorials

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[http://bit.ly/N-Bayes] How can we use Naive Bayes classifier with continuous (real-valued) attributes? We estimate the priors and the means / variances for the Gaussians (two in this example).
Views: 28070 Victor Lavrenko

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[http://bit.ly/N-Bayes] How can we distinguish spam from non-spam with a Naive Bayes classifier? We estimate the priors and multiple Bernoulli distributions for each class. Also learn how Naive Bayes can misclassify its own training examples.
Views: 31665 Victor Lavrenko

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In this third video text analytics in R, I've talked about modeling process using the naive bayes classifier that helps us creating a statistical text classifier model which helps classifying the data in ham or spam sms message. You will see how you can tune the parameters also and make the best use of naive bayes classifier model.

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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-478818537/m-482228628 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: 86184 Udacity

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Views: 1381 Data Science

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Naive Bayes | Naive Bayes Algorithm | Naive Bayes Algorithm In Data Mining ******************************************************* naive bayes, naive bayes classifier, naive bayes algorithm, naive bayes algorithm, naive bayes algorithm in data mining, naive bayes algorithm tutorial, naive bayes algorithm example, naive bayes algorithm explained, naive bayes model, naive bayes machine learning, naive bayes classifier python, naive bayes in r, naive bayes classifier in r, naive bayes algorithm is useful for, naive bayes assumption, naive bayes accuracy, naive bayes advantages, naive bayes example, naive bayes sklearn, naive bayes classifier example, Please Subscribe My Channel

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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: 148058 Last moment tuitions

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Full course: https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/?couponCode=DATATUBE We'll actually write a working spam classifier, using real email training data and a surprisingly small amount of code!
Views: 4924 Sundog Education

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Step by step on the next video http://lazeybutexpert.com

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Views: 6628 Shilpa Gusain

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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 53287 Udacity

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Implementation of Naive Bayes Classifier in R using dataset mushroom from the UCI repository. You may wanna add pakages e1071 and rminer in R because they were not present in R x64 3.3.1 by default. Music - Daft Punk - Instant Crush ft. Julian Casblancas

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This set of videos come from Andrew Ng's courses on Stanford OpenClassroom at http://openclassroom.stanford.edu/MainFolder/HomePage.php OpenClassroom is the predecessor of the famous MOOC platform Coursera. However, some of these videos are not published in Coursera Machine Learning course, i.e., Newton's Methods, Naive Bayes, etc. We selected some of them to share with you.
Views: 35600 Wang Zhiyang

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Demonstrating how to do Bayesian Classification, Nearest Neighbor, K means Clustering using WEKA . Generating data set and Probability Density Function using MATLAB. Important links: To know more about .arff formats go to: http://www.cs.waikato.ac.nz/ml/weka/arff.html Data sets: http://repository.seasr.org/Datasets/UCI/arff/ Online matlab: http://octave-online.net/
Views: 31489 Niranjan Singh

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The algorithm of choice, at least at a basic level, for text analysis is often the Naive Bayes classifier. Part of the reason for this is that text data is almost always massive in size. The Naive Bayes algorithm is so simple that it can be used at scale very easily with minimal process requirements. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 63501 sentdex

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This set of videos come from Andrew Ng's courses on Stanford OpenClassroom at http://openclassroom.stanford.edu/MainFolder/HomePage.php OpenClassroom is the predecessor of the famous MOOC platform Coursera. However, some of these videos are not published in Coursera Machine Learning course, i.e., Newton's Methods, Naive Bayes, etc. We selected some of them to share with you.
Views: 19636 Wang Zhiyang

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This video explains the concept of classification of text from a set of documents using a Naive Bayes Classifier approach. This video also deals with the concept of Bayes Theorem. We have explained the topic using a sample dataset of text which is classified as of whether it belongs to "sports" category or not. We train the model and then classify a new sentence 'A very close game' by finding its probability for belonging to "sports" category or not. The most likely probability is the final category, that sentence belongs to. Naive Bayes is a machine learning algorithm for classification problems. It is based on Bayes’ probability theorem. Naive Bayes classifier 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. Naive Bayes is not only known for its simplicity, but also for its effectiveness. Naive Bayes is fast to build models and make predictions with the Naive Bayes algorithm. Naive Bayes is the first algorithm that should be considered for solving a text classification problem. Hence, you should learn this algorithm thoroughly. For any queries or suggestions, Write to us at [email protected] We value your feedback. Thank You!! Visit Again!!
Views: 3848 Code Wrestling

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Hii there from Codegency! We are a team of young software developers and IT geeks who are always looking for challenges and ready to solve them, Feel free to contact us.. Do visit my instagram page and also like us on facebook, stay connected :) Instagram: https://www.instagram.com/code_gency/ Facebook: https://www.facebook.com/cgency/ Twitter : https://www.twitter.com/codegency Contact: +919769620035, +918108849398 For Blackbook Writeups & Descriptions: https://codegency.blogspot.in For Latest Notes & References: https://sites.google.com/view/itscholar/home
Views: 337 Codegency

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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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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 6: Multinomial Naïve Bayes http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 18451 WekaMOOC

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ABC
Views: 6032 zel buenaobra

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