SlideShare a Scribd company logo

Naive bayes

1 of 38
Download to read offline
PRESENTATION ON
NAÏVE BAYESIAN CLASSIFICATION
Presented By:




                                   http://ashrafsau.blogspot.in/
Ashraf Uddin
Sujit Singh
Chetanya Pratap Singh
South Asian University
(Master of Computer Application)
http://ashrafsau.blogspot.in/
OUTLINE

 Introduction   to Bayesian Classification
   Bayes Theorem
   Naïve Bayes Classifier




                                              http://ashrafsau.blogspot.in/
   Classification Example



 Text   Classification – an Application

 Comparison     with other classifiers
   Advantages and disadvantages
   Conclusions
CLASSIFICATION
 Classification:
     predicts categorical class labels




                                                             http://ashrafsau.blogspot.in/
     classifies data (constructs a model) based on the
      training set and the values (class labels) in a
      classifying attribute and uses it in classifying new
      data

 Typical   Applications
     credit approval
     target marketing
     medical diagnosis
     treatment effectiveness analysis
A TWO STEP PROCESS
   Model construction: describing a set of predetermined
    classes
     Each tuple/sample is assumed to belong to a predefined




                                                                             http://ashrafsau.blogspot.in/
      class, as determined by the class label attribute
     The set of tuples used for model construction: training set
     The model is represented as classification rules, decision
      trees, or mathematical formulae
   Model usage: for classifying future or unknown objects
       Estimate accuracy of the model
          The known label of test sample is compared with the
           classified result from the model
          Accuracy rate is the percentage of test set samples that
           are correctly classified by the model
          Test set is independent of training set, otherwise over-fitting
           will occur
INTRODUCTION TO BAYESIAN CLASSIFICATION

 What   is it ?




                                                            http://ashrafsau.blogspot.in/
    Statistical method for classification.
    Supervised Learning Method.
    Assumes an underlying probabilistic model, the Bayes
     theorem.
    Can solve problems involving both categorical and
     continuous valued attributes.
    Named after Thomas Bayes, who proposed the Bayes
     Theorem.
http://ashrafsau.blogspot.in/

Recommended

2.3 bayesian classification
2.3 bayesian classification2.3 bayesian classification
2.3 bayesian classificationKrish_ver2
 
Machine Learning With Logistic Regression
Machine Learning  With Logistic RegressionMachine Learning  With Logistic Regression
Machine Learning With Logistic RegressionKnoldus Inc.
 
Support vector machines (svm)
Support vector machines (svm)Support vector machines (svm)
Support vector machines (svm)Sharayu Patil
 
Machine Learning with Decision trees
Machine Learning with Decision treesMachine Learning with Decision trees
Machine Learning with Decision treesKnoldus Inc.
 
Decision trees in Machine Learning
Decision trees in Machine Learning Decision trees in Machine Learning
Decision trees in Machine Learning Mohammad Junaid Khan
 

More Related Content

What's hot

NAIVE BAYES CLASSIFIER
NAIVE BAYES CLASSIFIERNAIVE BAYES CLASSIFIER
NAIVE BAYES CLASSIFIERKnoldus Inc.
 
Analytical learning
Analytical learningAnalytical learning
Analytical learningswapnac12
 
Machine Learning and Real-World Applications
Machine Learning and Real-World ApplicationsMachine Learning and Real-World Applications
Machine Learning and Real-World ApplicationsMachinePulse
 
Ensemble learning
Ensemble learningEnsemble learning
Ensemble learningHaris Jamil
 
Decision tree induction \ Decision Tree Algorithm with Example| Data science
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceDecision tree induction \ Decision Tree Algorithm with Example| Data science
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceMaryamRehman6
 
Introduction to Deep Learning
Introduction to Deep LearningIntroduction to Deep Learning
Introduction to Deep LearningOswald Campesato
 
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
 
Semantic nets in artificial intelligence
Semantic nets in artificial intelligenceSemantic nets in artificial intelligence
Semantic nets in artificial intelligenceharshita virwani
 
An Introduction to Supervised Machine Learning and Pattern Classification: Th...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...An Introduction to Supervised Machine Learning and Pattern Classification: Th...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka
 
Inductive bias
Inductive biasInductive bias
Inductive biasswapnac12
 
Feedforward neural network
Feedforward neural networkFeedforward neural network
Feedforward neural networkSopheaktra YONG
 
K mean-clustering algorithm
K mean-clustering algorithmK mean-clustering algorithm
K mean-clustering algorithmparry prabhu
 
Bayseian decision theory
Bayseian decision theoryBayseian decision theory
Bayseian decision theorysia16
 
Artificial Neural Networks - ANN
Artificial Neural Networks - ANNArtificial Neural Networks - ANN
Artificial Neural Networks - ANNMohamed Talaat
 
Learning set of rules
Learning set of rulesLearning set of rules
Learning set of rulesswapnac12
 
Introduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnIntroduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnBenjamin Bengfort
 
Machine Learning presentation.
Machine Learning presentation.Machine Learning presentation.
Machine Learning presentation.butest
 
Decision Trees
Decision TreesDecision Trees
Decision TreesStudent
 
Dimensionality Reduction
Dimensionality ReductionDimensionality Reduction
Dimensionality Reductionmrizwan969
 

What's hot (20)

NAIVE BAYES CLASSIFIER
NAIVE BAYES CLASSIFIERNAIVE BAYES CLASSIFIER
NAIVE BAYES CLASSIFIER
 
K - Nearest neighbor ( KNN )
K - Nearest neighbor  ( KNN )K - Nearest neighbor  ( KNN )
K - Nearest neighbor ( KNN )
 
Analytical learning
Analytical learningAnalytical learning
Analytical learning
 
Machine Learning and Real-World Applications
Machine Learning and Real-World ApplicationsMachine Learning and Real-World Applications
Machine Learning and Real-World Applications
 
Ensemble learning
Ensemble learningEnsemble learning
Ensemble learning
 
Decision tree induction \ Decision Tree Algorithm with Example| Data science
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceDecision tree induction \ Decision Tree Algorithm with Example| Data science
Decision tree induction \ Decision Tree Algorithm with Example| Data science
 
Introduction to Deep Learning
Introduction to Deep LearningIntroduction to Deep Learning
Introduction to Deep Learning
 
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
 
Semantic nets in artificial intelligence
Semantic nets in artificial intelligenceSemantic nets in artificial intelligence
Semantic nets in artificial intelligence
 
An Introduction to Supervised Machine Learning and Pattern Classification: Th...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...An Introduction to Supervised Machine Learning and Pattern Classification: Th...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...
 
Inductive bias
Inductive biasInductive bias
Inductive bias
 
Feedforward neural network
Feedforward neural networkFeedforward neural network
Feedforward neural network
 
K mean-clustering algorithm
K mean-clustering algorithmK mean-clustering algorithm
K mean-clustering algorithm
 
Bayseian decision theory
Bayseian decision theoryBayseian decision theory
Bayseian decision theory
 
Artificial Neural Networks - ANN
Artificial Neural Networks - ANNArtificial Neural Networks - ANN
Artificial Neural Networks - ANN
 
Learning set of rules
Learning set of rulesLearning set of rules
Learning set of rules
 
Introduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnIntroduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-Learn
 
Machine Learning presentation.
Machine Learning presentation.Machine Learning presentation.
Machine Learning presentation.
 
Decision Trees
Decision TreesDecision Trees
Decision Trees
 
Dimensionality Reduction
Dimensionality ReductionDimensionality Reduction
Dimensionality Reduction
 

Viewers also liked

Introduction to text classification using naive bayes
Introduction to text classification using naive bayesIntroduction to text classification using naive bayes
Introduction to text classification using naive bayesDhwaj Raj
 
Lecture 5: Bayesian Classification
Lecture 5: Bayesian ClassificationLecture 5: Bayesian Classification
Lecture 5: Bayesian ClassificationMarina Santini
 
Dwdm naive bayes_ankit_gadgil_027
Dwdm naive bayes_ankit_gadgil_027Dwdm naive bayes_ankit_gadgil_027
Dwdm naive bayes_ankit_gadgil_027ankitgadgil
 
Genetic Algorithm by Example
Genetic Algorithm by ExampleGenetic Algorithm by Example
Genetic Algorithm by ExampleNobal Niraula
 
Data mining project presentation
Data mining project presentationData mining project presentation
Data mining project presentationKaiwen Qi
 

Viewers also liked (7)

Lecture10 - Naïve Bayes
Lecture10 - Naïve BayesLecture10 - Naïve Bayes
Lecture10 - Naïve Bayes
 
Introduction to text classification using naive bayes
Introduction to text classification using naive bayesIntroduction to text classification using naive bayes
Introduction to text classification using naive bayes
 
Lecture 5: Bayesian Classification
Lecture 5: Bayesian ClassificationLecture 5: Bayesian Classification
Lecture 5: Bayesian Classification
 
Dwdm naive bayes_ankit_gadgil_027
Dwdm naive bayes_ankit_gadgil_027Dwdm naive bayes_ankit_gadgil_027
Dwdm naive bayes_ankit_gadgil_027
 
ML KNN-ALGORITHM
ML KNN-ALGORITHMML KNN-ALGORITHM
ML KNN-ALGORITHM
 
Genetic Algorithm by Example
Genetic Algorithm by ExampleGenetic Algorithm by Example
Genetic Algorithm by Example
 
Data mining project presentation
Data mining project presentationData mining project presentation
Data mining project presentation
 

Similar to Naive bayes

Catégorisation automatisée de contenus documentaires : la ...
Catégorisation automatisée de contenus documentaires : la ...Catégorisation automatisée de contenus documentaires : la ...
Catégorisation automatisée de contenus documentaires : la ...butest
 
Introduction
IntroductionIntroduction
Introductionbutest
 
Introduction
IntroductionIntroduction
Introductionbutest
 
Introduction
IntroductionIntroduction
Introductionbutest
 
Classification with Naive Bayes
Classification with Naive BayesClassification with Naive Bayes
Classification with Naive BayesJosh Patterson
 
XL-Miner: Classification
XL-Miner: ClassificationXL-Miner: Classification
XL-Miner: Classificationxlminer content
 
Machine Learning with WEKA
Machine Learning with WEKAMachine Learning with WEKA
Machine Learning with WEKAbutest
 
Developing Knowledge-Based Systems
Developing Knowledge-Based SystemsDeveloping Knowledge-Based Systems
Developing Knowledge-Based SystemsAshique Rasool
 
lazy learners and other classication methods
lazy learners and other classication methodslazy learners and other classication methods
lazy learners and other classication methodsrajshreemuthiah
 
Introduction to EBSCOhost Research databases at University of Galway Library
Introduction to EBSCOhost Research databases at University of Galway Library Introduction to EBSCOhost Research databases at University of Galway Library
Introduction to EBSCOhost Research databases at University of Galway Library rosie.dunne
 
Textmining Predictive Models
Textmining Predictive ModelsTextmining Predictive Models
Textmining Predictive ModelsDatamining Tools
 
Textmining Predictive Models
Textmining Predictive ModelsTextmining Predictive Models
Textmining Predictive Modelsguest0edcaf
 
Alexandr Lyalyuk - PHP Machine Learning and user-oriented content
Alexandr Lyalyuk - PHP Machine Learning and user-oriented contentAlexandr Lyalyuk - PHP Machine Learning and user-oriented content
Alexandr Lyalyuk - PHP Machine Learning and user-oriented contentDrupalCamp Kyiv
 
Naive_hehe.pptx
Naive_hehe.pptxNaive_hehe.pptx
Naive_hehe.pptxMahimMajee
 

Similar to Naive bayes (20)

Supervised algorithms
Supervised algorithmsSupervised algorithms
Supervised algorithms
 
Naive bayes
Naive bayesNaive bayes
Naive bayes
 
Naive bayes classifier
Naive bayes classifierNaive bayes classifier
Naive bayes classifier
 
Catégorisation automatisée de contenus documentaires : la ...
Catégorisation automatisée de contenus documentaires : la ...Catégorisation automatisée de contenus documentaires : la ...
Catégorisation automatisée de contenus documentaires : la ...
 
Introduction
IntroductionIntroduction
Introduction
 
Introduction
IntroductionIntroduction
Introduction
 
Introduction
IntroductionIntroduction
Introduction
 
Classification with Naive Bayes
Classification with Naive BayesClassification with Naive Bayes
Classification with Naive Bayes
 
XL Miner: Classification
XL Miner: ClassificationXL Miner: Classification
XL Miner: Classification
 
XL-Miner: Classification
XL-Miner: ClassificationXL-Miner: Classification
XL-Miner: Classification
 
18 ijcse-01232
18 ijcse-0123218 ijcse-01232
18 ijcse-01232
 
Machine Learning with WEKA
Machine Learning with WEKAMachine Learning with WEKA
Machine Learning with WEKA
 
Developing Knowledge-Based Systems
Developing Knowledge-Based SystemsDeveloping Knowledge-Based Systems
Developing Knowledge-Based Systems
 
lazy learners and other classication methods
lazy learners and other classication methodslazy learners and other classication methods
lazy learners and other classication methods
 
Introduction to EBSCOhost Research databases at University of Galway Library
Introduction to EBSCOhost Research databases at University of Galway Library Introduction to EBSCOhost Research databases at University of Galway Library
Introduction to EBSCOhost Research databases at University of Galway Library
 
Textmining Predictive Models
Textmining Predictive ModelsTextmining Predictive Models
Textmining Predictive Models
 
Textmining Predictive Models
Textmining Predictive ModelsTextmining Predictive Models
Textmining Predictive Models
 
Textmining Predictive Models
Textmining Predictive ModelsTextmining Predictive Models
Textmining Predictive Models
 
Alexandr Lyalyuk - PHP Machine Learning and user-oriented content
Alexandr Lyalyuk - PHP Machine Learning and user-oriented contentAlexandr Lyalyuk - PHP Machine Learning and user-oriented content
Alexandr Lyalyuk - PHP Machine Learning and user-oriented content
 
Naive_hehe.pptx
Naive_hehe.pptxNaive_hehe.pptx
Naive_hehe.pptx
 

More from Ashraf Uddin

A short tutorial on r
A short tutorial on rA short tutorial on r
A short tutorial on rAshraf Uddin
 
Big Data: Its Characteristics And Architecture Capabilities
Big Data: Its Characteristics And Architecture CapabilitiesBig Data: Its Characteristics And Architecture Capabilities
Big Data: Its Characteristics And Architecture CapabilitiesAshraf Uddin
 
MapReduce: Simplified Data Processing on Large Clusters
MapReduce: Simplified Data Processing on Large ClustersMapReduce: Simplified Data Processing on Large Clusters
MapReduce: Simplified Data Processing on Large ClustersAshraf Uddin
 
Text Mining Infrastructure in R
Text Mining Infrastructure in RText Mining Infrastructure in R
Text Mining Infrastructure in RAshraf Uddin
 
Dynamic source routing
Dynamic source routingDynamic source routing
Dynamic source routingAshraf Uddin
 

More from Ashraf Uddin (7)

A short tutorial on r
A short tutorial on rA short tutorial on r
A short tutorial on r
 
Big Data: Its Characteristics And Architecture Capabilities
Big Data: Its Characteristics And Architecture CapabilitiesBig Data: Its Characteristics And Architecture Capabilities
Big Data: Its Characteristics And Architecture Capabilities
 
MapReduce: Simplified Data Processing on Large Clusters
MapReduce: Simplified Data Processing on Large ClustersMapReduce: Simplified Data Processing on Large Clusters
MapReduce: Simplified Data Processing on Large Clusters
 
Text Mining Infrastructure in R
Text Mining Infrastructure in RText Mining Infrastructure in R
Text Mining Infrastructure in R
 
Software piracy
Software piracySoftware piracy
Software piracy
 
Freenet
FreenetFreenet
Freenet
 
Dynamic source routing
Dynamic source routingDynamic source routing
Dynamic source routing
 

Recently uploaded

Nzinga Kika - The story of the queen
Nzinga Kika    -  The story of the queenNzinga Kika    -  The story of the queen
Nzinga Kika - The story of the queenDeanAmory1
 
Overview of Databases and Data Modelling-1.pdf
Overview of Databases and Data Modelling-1.pdfOverview of Databases and Data Modelling-1.pdf
Overview of Databases and Data Modelling-1.pdfChristalin Nelson
 
VPEC BROUCHER FOR ALL COURSES UPDATED FEB 2024
VPEC BROUCHER FOR ALL COURSES UPDATED FEB 2024VPEC BROUCHER FOR ALL COURSES UPDATED FEB 2024
VPEC BROUCHER FOR ALL COURSES UPDATED FEB 2024avesmalik2
 
Practical Research 1, Lesson 5: DESIGNING A RESEARCH PROJECT RELATED TO DAILY...
Practical Research 1, Lesson 5: DESIGNING A RESEARCH PROJECT RELATED TO DAILY...Practical Research 1, Lesson 5: DESIGNING A RESEARCH PROJECT RELATED TO DAILY...
Practical Research 1, Lesson 5: DESIGNING A RESEARCH PROJECT RELATED TO DAILY...Katherine Villaluna
 
Data Modeling - Entity Relationship Diagrams-1.pdf
Data Modeling - Entity Relationship Diagrams-1.pdfData Modeling - Entity Relationship Diagrams-1.pdf
Data Modeling - Entity Relationship Diagrams-1.pdfChristalin Nelson
 
Diploma 2nd yr PHARMACOLOGY chapter 5 part 1.pdf
Diploma 2nd yr PHARMACOLOGY chapter 5 part 1.pdfDiploma 2nd yr PHARMACOLOGY chapter 5 part 1.pdf
Diploma 2nd yr PHARMACOLOGY chapter 5 part 1.pdfSUMIT TIWARI
 
ICSE English Literature Class X Handwritten Notes
ICSE English Literature Class X Handwritten NotesICSE English Literature Class X Handwritten Notes
ICSE English Literature Class X Handwritten NotesGauri S
 
Dr.M.Florence Dayana-Cloud Computing-Unit - 1.pdf
Dr.M.Florence Dayana-Cloud Computing-Unit - 1.pdfDr.M.Florence Dayana-Cloud Computing-Unit - 1.pdf
Dr.M.Florence Dayana-Cloud Computing-Unit - 1.pdfDr.Florence Dayana
 
Overview of Databases and Data Modelling-2.pdf
Overview of Databases and Data Modelling-2.pdfOverview of Databases and Data Modelling-2.pdf
Overview of Databases and Data Modelling-2.pdfChristalin Nelson
 
Biology 152 - Topic Statement - Boolean Search
Biology 152 - Topic Statement - Boolean SearchBiology 152 - Topic Statement - Boolean Search
Biology 152 - Topic Statement - Boolean SearchRobert Tomaszewski
 
11 CI SINIF SINAQLARI - 5-2023-Aynura-Hamidova.pdf
11 CI SINIF SINAQLARI - 5-2023-Aynura-Hamidova.pdf11 CI SINIF SINAQLARI - 5-2023-Aynura-Hamidova.pdf
11 CI SINIF SINAQLARI - 5-2023-Aynura-Hamidova.pdfAynouraHamidova
 
A LABORATORY MANUAL FOR ORGANIC CHEMISTRY.pdf
A LABORATORY MANUAL FOR ORGANIC CHEMISTRY.pdfA LABORATORY MANUAL FOR ORGANIC CHEMISTRY.pdf
A LABORATORY MANUAL FOR ORGANIC CHEMISTRY.pdfDr.M.Geethavani
 
Plant Genetic Resources, Germplasm, gene pool - Copy.pptx
Plant Genetic Resources, Germplasm, gene pool - Copy.pptxPlant Genetic Resources, Germplasm, gene pool - Copy.pptx
Plant Genetic Resources, Germplasm, gene pool - Copy.pptxAKSHAYMAGAR17
 
spring_bee_bot_creations_erd primary.pdf
spring_bee_bot_creations_erd primary.pdfspring_bee_bot_creations_erd primary.pdf
spring_bee_bot_creations_erd primary.pdfKonstantina Koutsodimou
 
ACTIVIDAD DE CLASE No 1 sopa de letras.docx
ACTIVIDAD DE CLASE No 1 sopa de letras.docxACTIVIDAD DE CLASE No 1 sopa de letras.docx
ACTIVIDAD DE CLASE No 1 sopa de letras.docxMaria Lucia Céspedes
 
Food Web SlideShare for Ecology Notes Quiz in Canvas
Food Web SlideShare for Ecology Notes Quiz in CanvasFood Web SlideShare for Ecology Notes Quiz in Canvas
Food Web SlideShare for Ecology Notes Quiz in CanvasAlexandraSwartzwelde
 
Permeation enhancer of Transdermal drug delivery system
Permeation enhancer of Transdermal drug delivery systemPermeation enhancer of Transdermal drug delivery system
Permeation enhancer of Transdermal drug delivery systemchetanpatil2572000
 

Recently uploaded (20)

Nzinga Kika - The story of the queen
Nzinga Kika    -  The story of the queenNzinga Kika    -  The story of the queen
Nzinga Kika - The story of the queen
 
Overview of Databases and Data Modelling-1.pdf
Overview of Databases and Data Modelling-1.pdfOverview of Databases and Data Modelling-1.pdf
Overview of Databases and Data Modelling-1.pdf
 
VPEC BROUCHER FOR ALL COURSES UPDATED FEB 2024
VPEC BROUCHER FOR ALL COURSES UPDATED FEB 2024VPEC BROUCHER FOR ALL COURSES UPDATED FEB 2024
VPEC BROUCHER FOR ALL COURSES UPDATED FEB 2024
 
Practical Research 1, Lesson 5: DESIGNING A RESEARCH PROJECT RELATED TO DAILY...
Practical Research 1, Lesson 5: DESIGNING A RESEARCH PROJECT RELATED TO DAILY...Practical Research 1, Lesson 5: DESIGNING A RESEARCH PROJECT RELATED TO DAILY...
Practical Research 1, Lesson 5: DESIGNING A RESEARCH PROJECT RELATED TO DAILY...
 
Data Modeling - Entity Relationship Diagrams-1.pdf
Data Modeling - Entity Relationship Diagrams-1.pdfData Modeling - Entity Relationship Diagrams-1.pdf
Data Modeling - Entity Relationship Diagrams-1.pdf
 
Diploma 2nd yr PHARMACOLOGY chapter 5 part 1.pdf
Diploma 2nd yr PHARMACOLOGY chapter 5 part 1.pdfDiploma 2nd yr PHARMACOLOGY chapter 5 part 1.pdf
Diploma 2nd yr PHARMACOLOGY chapter 5 part 1.pdf
 
ICSE English Literature Class X Handwritten Notes
ICSE English Literature Class X Handwritten NotesICSE English Literature Class X Handwritten Notes
ICSE English Literature Class X Handwritten Notes
 
Dr.M.Florence Dayana-Cloud Computing-Unit - 1.pdf
Dr.M.Florence Dayana-Cloud Computing-Unit - 1.pdfDr.M.Florence Dayana-Cloud Computing-Unit - 1.pdf
Dr.M.Florence Dayana-Cloud Computing-Unit - 1.pdf
 
Advance Mobile Application Development class 04
Advance Mobile Application Development class 04Advance Mobile Application Development class 04
Advance Mobile Application Development class 04
 
Overview of Databases and Data Modelling-2.pdf
Overview of Databases and Data Modelling-2.pdfOverview of Databases and Data Modelling-2.pdf
Overview of Databases and Data Modelling-2.pdf
 
Lipids as Biopolymer
Lipids as Biopolymer Lipids as Biopolymer
Lipids as Biopolymer
 
Biology 152 - Topic Statement - Boolean Search
Biology 152 - Topic Statement - Boolean SearchBiology 152 - Topic Statement - Boolean Search
Biology 152 - Topic Statement - Boolean Search
 
11 CI SINIF SINAQLARI - 5-2023-Aynura-Hamidova.pdf
11 CI SINIF SINAQLARI - 5-2023-Aynura-Hamidova.pdf11 CI SINIF SINAQLARI - 5-2023-Aynura-Hamidova.pdf
11 CI SINIF SINAQLARI - 5-2023-Aynura-Hamidova.pdf
 
A LABORATORY MANUAL FOR ORGANIC CHEMISTRY.pdf
A LABORATORY MANUAL FOR ORGANIC CHEMISTRY.pdfA LABORATORY MANUAL FOR ORGANIC CHEMISTRY.pdf
A LABORATORY MANUAL FOR ORGANIC CHEMISTRY.pdf
 
Plant Genetic Resources, Germplasm, gene pool - Copy.pptx
Plant Genetic Resources, Germplasm, gene pool - Copy.pptxPlant Genetic Resources, Germplasm, gene pool - Copy.pptx
Plant Genetic Resources, Germplasm, gene pool - Copy.pptx
 
Capter 5 Climate of Ethiopia and the Horn GeES 1011.pdf
Capter 5 Climate of Ethiopia and the Horn GeES 1011.pdfCapter 5 Climate of Ethiopia and the Horn GeES 1011.pdf
Capter 5 Climate of Ethiopia and the Horn GeES 1011.pdf
 
spring_bee_bot_creations_erd primary.pdf
spring_bee_bot_creations_erd primary.pdfspring_bee_bot_creations_erd primary.pdf
spring_bee_bot_creations_erd primary.pdf
 
ACTIVIDAD DE CLASE No 1 sopa de letras.docx
ACTIVIDAD DE CLASE No 1 sopa de letras.docxACTIVIDAD DE CLASE No 1 sopa de letras.docx
ACTIVIDAD DE CLASE No 1 sopa de letras.docx
 
Food Web SlideShare for Ecology Notes Quiz in Canvas
Food Web SlideShare for Ecology Notes Quiz in CanvasFood Web SlideShare for Ecology Notes Quiz in Canvas
Food Web SlideShare for Ecology Notes Quiz in Canvas
 
Permeation enhancer of Transdermal drug delivery system
Permeation enhancer of Transdermal drug delivery systemPermeation enhancer of Transdermal drug delivery system
Permeation enhancer of Transdermal drug delivery system
 

Naive bayes

  • 1. PRESENTATION ON NAÏVE BAYESIAN CLASSIFICATION Presented By: http://ashrafsau.blogspot.in/ Ashraf Uddin Sujit Singh Chetanya Pratap Singh South Asian University (Master of Computer Application) http://ashrafsau.blogspot.in/
  • 2. OUTLINE  Introduction to Bayesian Classification  Bayes Theorem  Naïve Bayes Classifier http://ashrafsau.blogspot.in/  Classification Example  Text Classification – an Application  Comparison with other classifiers  Advantages and disadvantages  Conclusions
  • 3. CLASSIFICATION  Classification:  predicts categorical class labels http://ashrafsau.blogspot.in/  classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data  Typical Applications  credit approval  target marketing  medical diagnosis  treatment effectiveness analysis
  • 4. A TWO STEP PROCESS  Model construction: describing a set of predetermined classes  Each tuple/sample is assumed to belong to a predefined http://ashrafsau.blogspot.in/ class, as determined by the class label attribute  The set of tuples used for model construction: training set  The model is represented as classification rules, decision trees, or mathematical formulae  Model usage: for classifying future or unknown objects  Estimate accuracy of the model  The known label of test sample is compared with the classified result from the model  Accuracy rate is the percentage of test set samples that are correctly classified by the model  Test set is independent of training set, otherwise over-fitting will occur
  • 5. INTRODUCTION TO BAYESIAN CLASSIFICATION  What is it ? http://ashrafsau.blogspot.in/  Statistical method for classification.  Supervised Learning Method.  Assumes an underlying probabilistic model, the Bayes theorem.  Can solve problems involving both categorical and continuous valued attributes.  Named after Thomas Bayes, who proposed the Bayes Theorem.
  • 7. THE BAYES THEOREM  The Bayes Theorem:  P(H|X)= P(X|H) P(H)/ P(X) http://ashrafsau.blogspot.in/  P(H|X) : Probability that the customer will buy a computer given that we know his age, credit rating and income. (Posterior Probability of H)  P(H) : Probability that the customer will buy a computer regardless of age, credit rating, income (Prior Probability of H)  P(X|H) : Probability that the customer is 35 yrs old, have fair credit rating and earns $40,000, given that he has bought our computer (Posterior Probability of X)  P(X) : Probability that a person from our set of customers is 35 yrs old, have fair credit rating and earns $40,000. (Prior Probability of X)
  • 11. “ZERO” PROBLEM  What if there is a class, Ci, and X has an attribute value, xk, such that none of the samples in Ci has http://ashrafsau.blogspot.in/ that attribute value?  In that case P(xk|Ci) = 0, which results in P(X|Ci) = 0 even though P(xk|Ci) for all the other attributes in X may be large.
  • 12. NUMERICAL UNDERFLOW  When p(x|Y ) is often a very small number: the probability of observing any particular high- http://ashrafsau.blogspot.in/ dimensional vector is small.  This can lead to numerical under flow.
  • 14. BASIC ASSUMPTION  The Naïve Bayes assumption is that all the features are conditionally independent given the class label. http://ashrafsau.blogspot.in/  Even though this is usually false (since features are usually dependent)
  • 15. EXAMPLE: CLASS-LABELED TRAINING TUPLES FROM THE CUSTOMER DATABASE http://ashrafsau.blogspot.in/
  • 18. USES OF NAÏVE BAYES CLASSIFICATION  Text Classification  Spam Filtering http://ashrafsau.blogspot.in/  Hybrid Recommender System  Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource  Online Application  Simple Emotion Modeling
  • 19. TEXT CLASSIFICATION – AN APPLICATION OF NAIVE BAYES CLASSIFIER http://ashrafsau.blogspot.in/
  • 20. WHY TEXT CLASSIFICATION?  Learning which articles are of interest http://ashrafsau.blogspot.in/  Classify web pages by topic  Information extraction  Internet filters
  • 21. EXAMPLES OF TEXT CLASSIFICATION  CLASSES=BINARY  “spam” / “not spam” http://ashrafsau.blogspot.in/  CLASSES =TOPICS  “finance” / “sports” / “politics”  CLASSES =OPINION  “like” / “hate” / “neutral”  CLASSES =TOPICS  “AI” / “Theory” / “Graphics”  CLASSES =AUTHOR  “Shakespeare” / “Marlowe” / “Ben Jonson”
  • 22. EXAMPLES OF TEXT CLASSIFICATION  Classify news stories as world, business, SciTech, Sports ,Health etc  Classify email as spam / not spam http://ashrafsau.blogspot.in/  Classify business names by industry  Classify email to tech stuff as Mac, windows etc  Classify pdf files as research , other  Classify movie reviews as favorable, unfavorable, neutral  Classify documents  Classify technical papers as Interesting, Uninteresting  Classify Jokes as Funny, Not Funny  Classify web sites of companies by Standard Industrial Classification (SIC)
  • 23. NAÏVE BAYES APPROACH  Build the Vocabulary as the list of all distinct words that appear in all the documents of the training set. http://ashrafsau.blogspot.in/  Remove stop words and markings  The words in the vocabulary become the attributes, assuming that classification is independent of the positions of the words  Each document in the training set becomes a record with frequencies for each word in the Vocabulary.  Train the classifier based on the training data set, by computing the prior probabilities for each class and attributes.  Evaluate the results on Test data
  • 24. REPRESENTING TEXT: A LIST OF WORDS http://ashrafsau.blogspot.in/  Common Refinements: Remove Stop Words, Symbols
  • 25. TEXT CLASSIFICATION ALGORITHM: NAÏVE BAYES http://ashrafsau.blogspot.in/  Tct – Number of particular word in particular class  Tct’ – Number of total words in particular class  B’ – Number of distinct words in all class
  • 27. EXAMPLE CONT… http://ashrafsau.blogspot.in/ oProbability of Yes Class is more than that of No Class, Hence this document will go into Yes Class.
  • 28. Advantages and Disadvantages of Naïve Bayes Advantages : Easy to implement Requires a small amount of training data to estimate the parameters http://ashrafsau.blogspot.in/ Good results obtained in most of the cases Disadvantages: Assumption: class conditional independence, therefore loss of accuracy Practically, dependencies exist among variables E.g., hospitals: patients: Profile: age, family history, etc. Symptoms: fever, cough etc., Disease: lung cancer, diabetes, etc. Dependencies among these cannot be modelled by Naïve Bayesian Classifier
  • 29. An extension of Naive Bayes for delivering robust classifications •Naive assumption (statistical independent. of the features given the class) http://ashrafsau.blogspot.in/ •NBC computes a single posterior distribution. •However, the most probable class might depend on the chosen prior, especially on small data sets. •Prior-dependent classifications might be weak. Solution via set of probabilities: •Robust Bayes Classifier (Ramoni and Sebastiani, 2001) •Naive Credal Classifier (Zaffalon, 2001)
  • 30. •Violation of Independence Assumption http://ashrafsau.blogspot.in/ •Zero conditional probability Problem
  • 31. VIOLATION OF INDEPENDENCE ASSUMPTION  Naive Bayesian classifiers assume that the effect of an attribute value on a given class is independent http://ashrafsau.blogspot.in/ of the values of the other attributes. This assumption is called class conditional independence. It is made to simplify the computations involved and, in this sense, is considered “naive.”
  • 32. IMPROVEMENT  Bayesian belief network are graphical models, which unlike naïve Bayesian http://ashrafsau.blogspot.in/ classifiers, allow the representation of dependencies among subsets of attributes.  Bayesian belief networks can also be used for classification.
  • 33. ZERO CONDITIONAL PROBABILITY PROBLEM  If a given class and feature value never occur together in the training set then the frequency- http://ashrafsau.blogspot.in/ based probability estimate will be zero.  This is problematic since it will wipe out all information in the other probabilities when they are multiplied.  It is therefore often desirable to incorporate a small- sample correction in all probability estimates such that no probability is ever set to be exactly zero.
  • 34. CORRECTION  To eliminate zeros, we use add-one or Laplace smoothing, which simply adds http://ashrafsau.blogspot.in/ one to each count
  • 35. EXAMPLE  Suppose that for the class buys computer D (yes) in some training database, D, containing 1000 tuples.  we have 0 tuples with income D low,  990 tuples with income D medium, and  10 tuples with income D high. http://ashrafsau.blogspot.in/  The probabilities of these events, without the Laplacian correction, are 0, 0.990 (from 990/1000), and 0.010 (from 10/1000), respectively.  Using the Laplacian correction for the three quantities, we pretend that we have 1 more tuple for each income-value pair. In this way, we instead obtain the following probabilities : respectively. The “corrected” probability estimates are close to their “uncorrected” counterparts, yet the zero probability value is avoided.
  • 36. Remarks on the Naive Bayesian Classifier •Studies comparing classification algorithms have found that the naive Bayesian classifier to be comparable in http://ashrafsau.blogspot.in/ performance with decision tree and selected neural network classifiers. •Bayesian classifiers have also exhibited high accuracy and speed when applied to large databases.
  • 37. Conclusions The naive Bayes model is tremendously appealing because of its simplicity, elegance, and robustness.  It is one of the oldest formal classification algorithms, and yet even in http://ashrafsau.blogspot.in/ its simplest form it is often surprisingly effective. It is widely used in areas such as text classification and spam filtering. A large number of modifications have been introduced, by the statistical, data mining, machine learning, and pattern recognition communities, in an attempt to make it more flexible.  but some one has to recognize that such modifications are necessarily complications, which detract from its basic simplicity.
  • 38. REFERENCES  http://en.wikipedia.org/wiki/Naive_Bayes_classifier  http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo- http://ashrafsau.blogspot.in/ 20/www/mlbook/ch6.pdf  Data Mining: Concepts and Techniques, 3rd Edition, Han & Kamber & Pei ISBN: 9780123 814791