SlideShare ist ein Scribd-Unternehmen logo
1 von 12
2
3
5.5.1


        Theorem)                  n
        X = (T1 = x1 )∧(T2 = x2 )∧· · ·∧(Tn = xn )
   CH X               C = {C1 , C2 , ...}
                    X                       CH
                  X              CH                  5.3
               P (CH ∩ X)    P (X | CH )P (CH )
P (CH   | X) =
           CH             =
                          (C = )
                  P (X)            P (X)
        P (CH |A)                  A
                          P (CH |X)

            5.4
P (CH ∩ X) = P (CH | X)P (X) = PP (C| CHX) (CH ) 4 H )
                                (X H ∩ )P P (X|C
P (CH ∩ X)   P (X | CH )P (CH )
P (CH   | X) =            =
                  P (X)           P (X)
P (C =   | X) > P (C = × | X)




P (C =   | X) < P (C = × | X)




                                6
P (X | C )P (C )
   P (C   | X) =
                          P (X)
        P (C ) = N /N

           X   = (T1 = x1 ) ∧ (T2 = x2 ) ∧ · · · ∧ (Tn = xn )
               = x1 ∧ x2 ∧ · · · ∧ x3

   P (X | C ) = P (x1 ∧ x2 ∧ · · · ∧ xn | C )
              = P (x1 | C )P (x2 | C ) · · · P (xn | C )
                    n
               =         P (xk | C )
                   k=1

P (X)        CH
P (C ) = 4/10 = 0.4, P (C× ) = 6/10 = 0.6
X = (T1 = No) ∧ (T2 = No) ∧ (T3 = Yes) ∧ (T4 = Yes)
                                                      8
X = (T1 = No) ∧ (T2 = No) ∧ (T3 = Yes) ∧ (T4 = Yes)
   P (X | C )   = P (T1 = No | C ) × P (T2 = No | C )
                  ×P (T3 = Yes) | C ) × P (T4 = Yes | C )

      P (T1 = No | C    )   =   2/4 = 0.5
      P (T2 = No | C    )   =   0/4 = 0.0
      P (T3 = Yes | C   )   =   2/4 = 0.5
      P (T4 = Yes | C   )   =   0/4 = 0.0

      P (X|C ) = 0.5 × 0.0 × 0.5 × 0.0 = 0.0
      P (X | C ) · P (C ) = 0.0 × 0.4 = 0.0
X = (T1 = No) ∧ (T2 = No) ∧ (T3 = Yes) ∧ (T4 = Yes)
P (X | C× )   =   P (T1 = No | C× ) × P (T2 = No | C× )×
                  P (T3 = Yes | C× ) × P (T4 = Yes | C× )


     P (T1 = No | C× )     =   4/6 = 0.667
     P (T2 = No | C× )     =   4/6 = 0.667
     P (T3 = Yes | C× )    =   1/6 = 0.167
     P (T4 = Yes | C× )    =   4/6 = 0.667

   P (X|C× ) = 0.667 × 0.667 × 0.167 × 0.667 = 0.0494
   P (X | C× ) · P (C× ) = 0.0494 × 0.4 = 0.0198
P (X | C ) · P (C ) = 0.0
  P (X | C× ) · P (C× ) = 0.0198

P (X | C ) · P (C ) < (X | C× ) · P (C× )




                                            11
12

Weitere ähnliche Inhalte

Was ist angesagt?

Number Theory - HCF basics
Number Theory - HCF basicsNumber Theory - HCF basics
Number Theory - HCF basics2IIM
 
Polynomials - Possible pairs of Solutions
Polynomials - Possible pairs of SolutionsPolynomials - Possible pairs of Solutions
Polynomials - Possible pairs of Solutions2IIM
 
INEQUALITIES - INTEGER
INEQUALITIES - INTEGERINEQUALITIES - INTEGER
INEQUALITIES - INTEGER2IIM
 
A derivation of the sampling formulas for An Entity-Topic Model for Entity Li...
A derivation of the sampling formulas for An Entity-Topic Model for Entity Li...A derivation of the sampling formulas for An Entity-Topic Model for Entity Li...
A derivation of the sampling formulas for An Entity-Topic Model for Entity Li...Tomonari Masada
 
Zap Q-Learning - ISMP 2018
Zap Q-Learning - ISMP 2018Zap Q-Learning - ISMP 2018
Zap Q-Learning - ISMP 2018Sean Meyn
 
A new Perron-Frobenius theorem for nonnegative tensors
A new Perron-Frobenius theorem for nonnegative tensorsA new Perron-Frobenius theorem for nonnegative tensors
A new Perron-Frobenius theorem for nonnegative tensorsFrancesco Tudisco
 
Variational AutoEncoder
Variational AutoEncoderVariational AutoEncoder
Variational AutoEncoderKazuki Nitta
 
25285 mws gen_int_ppt_trapcontinuous
25285 mws gen_int_ppt_trapcontinuous25285 mws gen_int_ppt_trapcontinuous
25285 mws gen_int_ppt_trapcontinuousJyoti Parange
 
Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...
Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...
Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...Francesco Tudisco
 
Day 9 examples u4w14
Day 9 examples u4w14Day 9 examples u4w14
Day 9 examples u4w14jchartiersjsd
 
APPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONAPPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONDhrupal Patel
 
6.1 & 6.4 an overview of the area problem area
6.1 & 6.4 an overview of the area problem area6.1 & 6.4 an overview of the area problem area
6.1 & 6.4 an overview of the area problem areadicosmo178
 
Numerical integration based on the hyperfunction theory
Numerical integration based on the hyperfunction theoryNumerical integration based on the hyperfunction theory
Numerical integration based on the hyperfunction theoryHidenoriOgata
 
Spectral Continuity: (p, r) - Α P And (p, k) - Q
Spectral Continuity: (p, r) - Α P And (p, k) - QSpectral Continuity: (p, r) - Α P And (p, k) - Q
Spectral Continuity: (p, r) - Α P And (p, k) - QIOSR Journals
 
Day 6 examples u2w14
Day 6 examples u2w14Day 6 examples u2w14
Day 6 examples u2w14jchartiersjsd
 

Was ist angesagt? (20)

Number Theory - HCF basics
Number Theory - HCF basicsNumber Theory - HCF basics
Number Theory - HCF basics
 
Polynomials - Possible pairs of Solutions
Polynomials - Possible pairs of SolutionsPolynomials - Possible pairs of Solutions
Polynomials - Possible pairs of Solutions
 
Slides simplexe
Slides simplexeSlides simplexe
Slides simplexe
 
INEQUALITIES - INTEGER
INEQUALITIES - INTEGERINEQUALITIES - INTEGER
INEQUALITIES - INTEGER
 
A derivation of the sampling formulas for An Entity-Topic Model for Entity Li...
A derivation of the sampling formulas for An Entity-Topic Model for Entity Li...A derivation of the sampling formulas for An Entity-Topic Model for Entity Li...
A derivation of the sampling formulas for An Entity-Topic Model for Entity Li...
 
Zap Q-Learning - ISMP 2018
Zap Q-Learning - ISMP 2018Zap Q-Learning - ISMP 2018
Zap Q-Learning - ISMP 2018
 
A new Perron-Frobenius theorem for nonnegative tensors
A new Perron-Frobenius theorem for nonnegative tensorsA new Perron-Frobenius theorem for nonnegative tensors
A new Perron-Frobenius theorem for nonnegative tensors
 
Slides econ-lm
Slides econ-lmSlides econ-lm
Slides econ-lm
 
Slides econ-lm
Slides econ-lmSlides econ-lm
Slides econ-lm
 
the ABC of ABC
the ABC of ABCthe ABC of ABC
the ABC of ABC
 
Variational AutoEncoder
Variational AutoEncoderVariational AutoEncoder
Variational AutoEncoder
 
ABC-Gibbs
ABC-GibbsABC-Gibbs
ABC-Gibbs
 
25285 mws gen_int_ppt_trapcontinuous
25285 mws gen_int_ppt_trapcontinuous25285 mws gen_int_ppt_trapcontinuous
25285 mws gen_int_ppt_trapcontinuous
 
Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...
Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...
Nodal Domain Theorem for the p-Laplacian on Graphs and the Related Multiway C...
 
Day 9 examples u4w14
Day 9 examples u4w14Day 9 examples u4w14
Day 9 examples u4w14
 
APPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONAPPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATION
 
6.1 & 6.4 an overview of the area problem area
6.1 & 6.4 an overview of the area problem area6.1 & 6.4 an overview of the area problem area
6.1 & 6.4 an overview of the area problem area
 
Numerical integration based on the hyperfunction theory
Numerical integration based on the hyperfunction theoryNumerical integration based on the hyperfunction theory
Numerical integration based on the hyperfunction theory
 
Spectral Continuity: (p, r) - Α P And (p, k) - Q
Spectral Continuity: (p, r) - Α P And (p, k) - QSpectral Continuity: (p, r) - Α P And (p, k) - Q
Spectral Continuity: (p, r) - Α P And (p, k) - Q
 
Day 6 examples u2w14
Day 6 examples u2w14Day 6 examples u2w14
Day 6 examples u2w14
 

Andere mochten auch

Datamining 9th Association Rule
Datamining 9th Association RuleDatamining 9th Association Rule
Datamining 9th Association Rulesesejun
 
Datamining 2nd decisiontree
Datamining 2nd decisiontreeDatamining 2nd decisiontree
Datamining 2nd decisiontreesesejun
 
Datamining 5th Knn
Datamining 5th KnnDatamining 5th Knn
Datamining 5th Knnsesejun
 
bioinfolec_20070706 4th
bioinfolec_20070706 4thbioinfolec_20070706 4th
bioinfolec_20070706 4thsesejun
 
Ohp Seijoen H20 02 Hensu To Kata
Ohp Seijoen H20 02 Hensu To KataOhp Seijoen H20 02 Hensu To Kata
Ohp Seijoen H20 02 Hensu To Katasesejun
 
Datamining r 2nd
Datamining r 2ndDatamining r 2nd
Datamining r 2ndsesejun
 
Datamining 5th knn
Datamining 5th knnDatamining 5th knn
Datamining 5th knnsesejun
 

Andere mochten auch (9)

Datamining 9th Association Rule
Datamining 9th Association RuleDatamining 9th Association Rule
Datamining 9th Association Rule
 
080806
080806080806
080806
 
Datamining 2nd decisiontree
Datamining 2nd decisiontreeDatamining 2nd decisiontree
Datamining 2nd decisiontree
 
Datamining 5th Knn
Datamining 5th KnnDatamining 5th Knn
Datamining 5th Knn
 
bioinfolec_20070706 4th
bioinfolec_20070706 4thbioinfolec_20070706 4th
bioinfolec_20070706 4th
 
Ohp Seijoen H20 02 Hensu To Kata
Ohp Seijoen H20 02 Hensu To KataOhp Seijoen H20 02 Hensu To Kata
Ohp Seijoen H20 02 Hensu To Kata
 
Datamining r 2nd
Datamining r 2ndDatamining r 2nd
Datamining r 2nd
 
Datamining 5th knn
Datamining 5th knnDatamining 5th knn
Datamining 5th knn
 
080806
080806080806
080806
 

Ähnlich wie Datamining 3rd Naivebayes

Math34 Trigonometric Formulas
Math34 Trigonometric  FormulasMath34 Trigonometric  Formulas
Math34 Trigonometric FormulasTopTuition
 
Query Suggestion @ tokyotextmining#2
Query Suggestion @ tokyotextmining#2Query Suggestion @ tokyotextmining#2
Query Suggestion @ tokyotextmining#2ybenjo
 
cps170_bayes_nets.ppt
cps170_bayes_nets.pptcps170_bayes_nets.ppt
cps170_bayes_nets.pptFaizAbaas
 
Chapter 3 – Random Variables and Probability Distributions
Chapter 3 – Random Variables and Probability DistributionsChapter 3 – Random Variables and Probability Distributions
Chapter 3 – Random Variables and Probability DistributionsJasonTagapanGulla
 
Testing for mixtures at BNP 13
Testing for mixtures at BNP 13Testing for mixtures at BNP 13
Testing for mixtures at BNP 13Christian Robert
 
Model Selection with Piecewise Regular Gauges
Model Selection with Piecewise Regular GaugesModel Selection with Piecewise Regular Gauges
Model Selection with Piecewise Regular GaugesGabriel Peyré
 
Low Complexity Regularization of Inverse Problems
Low Complexity Regularization of Inverse ProblemsLow Complexity Regularization of Inverse Problems
Low Complexity Regularization of Inverse ProblemsGabriel Peyré
 
How many components in a mixture?
How many components in a mixture?How many components in a mixture?
How many components in a mixture?Christian Robert
 
Math34 Trigonometric Formulas
Math34 Trigonometric  FormulasMath34 Trigonometric  Formulas
Math34 Trigonometric FormulasTopTuition
 
Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms
Reinforcement Learning: Hidden Theory and New Super-Fast AlgorithmsReinforcement Learning: Hidden Theory and New Super-Fast Algorithms
Reinforcement Learning: Hidden Theory and New Super-Fast AlgorithmsSean Meyn
 
Binomial distribution
Binomial distributionBinomial distribution
Binomial distributionMoloy De
 
Scattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysisScattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysisVjekoslavKovac1
 
Math34 Trigonometric Formulas
Math34 Trigonometric  FormulasMath34 Trigonometric  Formulas
Math34 Trigonometric FormulasTopTuition
 
Mathematical formula tables
Mathematical formula tablesMathematical formula tables
Mathematical formula tablesSaravana Selvan
 
Hybrid Atlas Models of Financial Equity Market
Hybrid Atlas Models of Financial Equity MarketHybrid Atlas Models of Financial Equity Market
Hybrid Atlas Models of Financial Equity Markettomoyukiichiba
 

Ähnlich wie Datamining 3rd Naivebayes (20)

Math34 Trigonometric Formulas
Math34 Trigonometric  FormulasMath34 Trigonometric  Formulas
Math34 Trigonometric Formulas
 
Query Suggestion @ tokyotextmining#2
Query Suggestion @ tokyotextmining#2Query Suggestion @ tokyotextmining#2
Query Suggestion @ tokyotextmining#2
 
cps170_bayes_nets.ppt
cps170_bayes_nets.pptcps170_bayes_nets.ppt
cps170_bayes_nets.ppt
 
Chapter 3 – Random Variables and Probability Distributions
Chapter 3 – Random Variables and Probability DistributionsChapter 3 – Random Variables and Probability Distributions
Chapter 3 – Random Variables and Probability Distributions
 
Testing for mixtures at BNP 13
Testing for mixtures at BNP 13Testing for mixtures at BNP 13
Testing for mixtures at BNP 13
 
Model Selection with Piecewise Regular Gauges
Model Selection with Piecewise Regular GaugesModel Selection with Piecewise Regular Gauges
Model Selection with Piecewise Regular Gauges
 
Low Complexity Regularization of Inverse Problems
Low Complexity Regularization of Inverse ProblemsLow Complexity Regularization of Inverse Problems
Low Complexity Regularization of Inverse Problems
 
How many components in a mixture?
How many components in a mixture?How many components in a mixture?
How many components in a mixture?
 
Math34 Trigonometric Formulas
Math34 Trigonometric  FormulasMath34 Trigonometric  Formulas
Math34 Trigonometric Formulas
 
Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms
Reinforcement Learning: Hidden Theory and New Super-Fast AlgorithmsReinforcement Learning: Hidden Theory and New Super-Fast Algorithms
Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms
 
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
 
Sect5 6
Sect5 6Sect5 6
Sect5 6
 
Sect2 1
Sect2 1Sect2 1
Sect2 1
 
Section1 stochastic
Section1 stochasticSection1 stochastic
Section1 stochastic
 
Elliptic Curves
Elliptic CurvesElliptic Curves
Elliptic Curves
 
Binomial distribution
Binomial distributionBinomial distribution
Binomial distribution
 
Scattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysisScattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysis
 
Math34 Trigonometric Formulas
Math34 Trigonometric  FormulasMath34 Trigonometric  Formulas
Math34 Trigonometric Formulas
 
Mathematical formula tables
Mathematical formula tablesMathematical formula tables
Mathematical formula tables
 
Hybrid Atlas Models of Financial Equity Market
Hybrid Atlas Models of Financial Equity MarketHybrid Atlas Models of Financial Equity Market
Hybrid Atlas Models of Financial Equity Market
 

Mehr von sesejun

RNAseqによる変動遺伝子抽出の統計: A Review
RNAseqによる変動遺伝子抽出の統計: A ReviewRNAseqによる変動遺伝子抽出の統計: A Review
RNAseqによる変動遺伝子抽出の統計: A Reviewsesejun
 
バイオインフォマティクスによる遺伝子発現解析
バイオインフォマティクスによる遺伝子発現解析バイオインフォマティクスによる遺伝子発現解析
バイオインフォマティクスによる遺伝子発現解析sesejun
 
次世代シーケンサが求める機械学習
次世代シーケンサが求める機械学習次世代シーケンサが求める機械学習
次世代シーケンサが求める機械学習sesejun
 
20110602labseminar pub
20110602labseminar pub20110602labseminar pub
20110602labseminar pubsesejun
 
20110524zurichngs 2nd pub
20110524zurichngs 2nd pub20110524zurichngs 2nd pub
20110524zurichngs 2nd pubsesejun
 
20110524zurichngs 1st pub
20110524zurichngs 1st pub20110524zurichngs 1st pub
20110524zurichngs 1st pubsesejun
 
20110214nips2010 read
20110214nips2010 read20110214nips2010 read
20110214nips2010 readsesejun
 
Datamining 9th association_rule.key
Datamining 9th association_rule.keyDatamining 9th association_rule.key
Datamining 9th association_rule.keysesejun
 
Datamining 8th hclustering
Datamining 8th hclusteringDatamining 8th hclustering
Datamining 8th hclusteringsesejun
 
Datamining r 4th
Datamining r 4thDatamining r 4th
Datamining r 4thsesejun
 
Datamining r 3rd
Datamining r 3rdDatamining r 3rd
Datamining r 3rdsesejun
 
Datamining r 1st
Datamining r 1stDatamining r 1st
Datamining r 1stsesejun
 
Datamining 6th svm
Datamining 6th svmDatamining 6th svm
Datamining 6th svmsesejun
 
Datamining 4th adaboost
Datamining 4th adaboostDatamining 4th adaboost
Datamining 4th adaboostsesejun
 
Datamining 3rd naivebayes
Datamining 3rd naivebayesDatamining 3rd naivebayes
Datamining 3rd naivebayessesejun
 
Datamining 7th kmeans
Datamining 7th kmeansDatamining 7th kmeans
Datamining 7th kmeanssesejun
 
100401 Bioinfoinfra
100401 Bioinfoinfra100401 Bioinfoinfra
100401 Bioinfoinfrasesejun
 
Datamining 8th Hclustering
Datamining 8th HclusteringDatamining 8th Hclustering
Datamining 8th Hclusteringsesejun
 
Datamining 9th Association Rule
Datamining 9th Association RuleDatamining 9th Association Rule
Datamining 9th Association Rulesesejun
 
Datamining 8th Hclustering
Datamining 8th HclusteringDatamining 8th Hclustering
Datamining 8th Hclusteringsesejun
 

Mehr von sesejun (20)

RNAseqによる変動遺伝子抽出の統計: A Review
RNAseqによる変動遺伝子抽出の統計: A ReviewRNAseqによる変動遺伝子抽出の統計: A Review
RNAseqによる変動遺伝子抽出の統計: A Review
 
バイオインフォマティクスによる遺伝子発現解析
バイオインフォマティクスによる遺伝子発現解析バイオインフォマティクスによる遺伝子発現解析
バイオインフォマティクスによる遺伝子発現解析
 
次世代シーケンサが求める機械学習
次世代シーケンサが求める機械学習次世代シーケンサが求める機械学習
次世代シーケンサが求める機械学習
 
20110602labseminar pub
20110602labseminar pub20110602labseminar pub
20110602labseminar pub
 
20110524zurichngs 2nd pub
20110524zurichngs 2nd pub20110524zurichngs 2nd pub
20110524zurichngs 2nd pub
 
20110524zurichngs 1st pub
20110524zurichngs 1st pub20110524zurichngs 1st pub
20110524zurichngs 1st pub
 
20110214nips2010 read
20110214nips2010 read20110214nips2010 read
20110214nips2010 read
 
Datamining 9th association_rule.key
Datamining 9th association_rule.keyDatamining 9th association_rule.key
Datamining 9th association_rule.key
 
Datamining 8th hclustering
Datamining 8th hclusteringDatamining 8th hclustering
Datamining 8th hclustering
 
Datamining r 4th
Datamining r 4thDatamining r 4th
Datamining r 4th
 
Datamining r 3rd
Datamining r 3rdDatamining r 3rd
Datamining r 3rd
 
Datamining r 1st
Datamining r 1stDatamining r 1st
Datamining r 1st
 
Datamining 6th svm
Datamining 6th svmDatamining 6th svm
Datamining 6th svm
 
Datamining 4th adaboost
Datamining 4th adaboostDatamining 4th adaboost
Datamining 4th adaboost
 
Datamining 3rd naivebayes
Datamining 3rd naivebayesDatamining 3rd naivebayes
Datamining 3rd naivebayes
 
Datamining 7th kmeans
Datamining 7th kmeansDatamining 7th kmeans
Datamining 7th kmeans
 
100401 Bioinfoinfra
100401 Bioinfoinfra100401 Bioinfoinfra
100401 Bioinfoinfra
 
Datamining 8th Hclustering
Datamining 8th HclusteringDatamining 8th Hclustering
Datamining 8th Hclustering
 
Datamining 9th Association Rule
Datamining 9th Association RuleDatamining 9th Association Rule
Datamining 9th Association Rule
 
Datamining 8th Hclustering
Datamining 8th HclusteringDatamining 8th Hclustering
Datamining 8th Hclustering
 

Kürzlich hochgeladen

Call Girls Pune Just Call 9907093804 Top Class Call Girl Service Available
Call Girls Pune Just Call 9907093804 Top Class Call Girl Service AvailableCall Girls Pune Just Call 9907093804 Top Class Call Girl Service Available
Call Girls Pune Just Call 9907093804 Top Class Call Girl Service AvailableDipal Arora
 
Progress Report - Oracle Database Analyst Summit
Progress  Report - Oracle Database Analyst SummitProgress  Report - Oracle Database Analyst Summit
Progress Report - Oracle Database Analyst SummitHolger Mueller
 
Mondelez State of Snacking and Future Trends 2023
Mondelez State of Snacking and Future Trends 2023Mondelez State of Snacking and Future Trends 2023
Mondelez State of Snacking and Future Trends 2023Neil Kimberley
 
Monte Carlo simulation : Simulation using MCSM
Monte Carlo simulation : Simulation using MCSMMonte Carlo simulation : Simulation using MCSM
Monte Carlo simulation : Simulation using MCSMRavindra Nath Shukla
 
Regression analysis: Simple Linear Regression Multiple Linear Regression
Regression analysis:  Simple Linear Regression Multiple Linear RegressionRegression analysis:  Simple Linear Regression Multiple Linear Regression
Regression analysis: Simple Linear Regression Multiple Linear RegressionRavindra Nath Shukla
 
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...anilsa9823
 
Call Girls Navi Mumbai Just Call 9907093804 Top Class Call Girl Service Avail...
Call Girls Navi Mumbai Just Call 9907093804 Top Class Call Girl Service Avail...Call Girls Navi Mumbai Just Call 9907093804 Top Class Call Girl Service Avail...
Call Girls Navi Mumbai Just Call 9907093804 Top Class Call Girl Service Avail...Dipal Arora
 
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...Any kyc Account
 
Call Girls In Panjim North Goa 9971646499 Genuine Service
Call Girls In Panjim North Goa 9971646499 Genuine ServiceCall Girls In Panjim North Goa 9971646499 Genuine Service
Call Girls In Panjim North Goa 9971646499 Genuine Serviceritikaroy0888
 
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876dlhescort
 
Famous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st CenturyFamous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st Centuryrwgiffor
 
Call Girls in Gomti Nagar - 7388211116 - With room Service
Call Girls in Gomti Nagar - 7388211116  - With room ServiceCall Girls in Gomti Nagar - 7388211116  - With room Service
Call Girls in Gomti Nagar - 7388211116 - With room Servicediscovermytutordmt
 
Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...
Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...
Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...Dave Litwiller
 
Ensure the security of your HCL environment by applying the Zero Trust princi...
Ensure the security of your HCL environment by applying the Zero Trust princi...Ensure the security of your HCL environment by applying the Zero Trust princi...
Ensure the security of your HCL environment by applying the Zero Trust princi...Roland Driesen
 
VIP Call Girls In Saharaganj ( Lucknow ) 🔝 8923113531 🔝 Cash Payment (COD) 👒
VIP Call Girls In Saharaganj ( Lucknow  ) 🔝 8923113531 🔝  Cash Payment (COD) 👒VIP Call Girls In Saharaganj ( Lucknow  ) 🔝 8923113531 🔝  Cash Payment (COD) 👒
VIP Call Girls In Saharaganj ( Lucknow ) 🔝 8923113531 🔝 Cash Payment (COD) 👒anilsa9823
 
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...Aggregage
 
Insurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usageInsurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usageMatteo Carbone
 
RSA Conference Exhibitor List 2024 - Exhibitors Data
RSA Conference Exhibitor List 2024 - Exhibitors DataRSA Conference Exhibitor List 2024 - Exhibitors Data
RSA Conference Exhibitor List 2024 - Exhibitors DataExhibitors Data
 
Best Basmati Rice Manufacturers in India
Best Basmati Rice Manufacturers in IndiaBest Basmati Rice Manufacturers in India
Best Basmati Rice Manufacturers in IndiaShree Krishna Exports
 

Kürzlich hochgeladen (20)

Call Girls Pune Just Call 9907093804 Top Class Call Girl Service Available
Call Girls Pune Just Call 9907093804 Top Class Call Girl Service AvailableCall Girls Pune Just Call 9907093804 Top Class Call Girl Service Available
Call Girls Pune Just Call 9907093804 Top Class Call Girl Service Available
 
Progress Report - Oracle Database Analyst Summit
Progress  Report - Oracle Database Analyst SummitProgress  Report - Oracle Database Analyst Summit
Progress Report - Oracle Database Analyst Summit
 
Mondelez State of Snacking and Future Trends 2023
Mondelez State of Snacking and Future Trends 2023Mondelez State of Snacking and Future Trends 2023
Mondelez State of Snacking and Future Trends 2023
 
Monte Carlo simulation : Simulation using MCSM
Monte Carlo simulation : Simulation using MCSMMonte Carlo simulation : Simulation using MCSM
Monte Carlo simulation : Simulation using MCSM
 
Regression analysis: Simple Linear Regression Multiple Linear Regression
Regression analysis:  Simple Linear Regression Multiple Linear RegressionRegression analysis:  Simple Linear Regression Multiple Linear Regression
Regression analysis: Simple Linear Regression Multiple Linear Regression
 
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...
 
Call Girls Navi Mumbai Just Call 9907093804 Top Class Call Girl Service Avail...
Call Girls Navi Mumbai Just Call 9907093804 Top Class Call Girl Service Avail...Call Girls Navi Mumbai Just Call 9907093804 Top Class Call Girl Service Avail...
Call Girls Navi Mumbai Just Call 9907093804 Top Class Call Girl Service Avail...
 
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...
 
Call Girls In Panjim North Goa 9971646499 Genuine Service
Call Girls In Panjim North Goa 9971646499 Genuine ServiceCall Girls In Panjim North Goa 9971646499 Genuine Service
Call Girls In Panjim North Goa 9971646499 Genuine Service
 
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
 
Famous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st CenturyFamous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st Century
 
Call Girls in Gomti Nagar - 7388211116 - With room Service
Call Girls in Gomti Nagar - 7388211116  - With room ServiceCall Girls in Gomti Nagar - 7388211116  - With room Service
Call Girls in Gomti Nagar - 7388211116 - With room Service
 
Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...
Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...
Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...
 
Ensure the security of your HCL environment by applying the Zero Trust princi...
Ensure the security of your HCL environment by applying the Zero Trust princi...Ensure the security of your HCL environment by applying the Zero Trust princi...
Ensure the security of your HCL environment by applying the Zero Trust princi...
 
unwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabi
unwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabiunwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabi
unwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabi
 
VIP Call Girls In Saharaganj ( Lucknow ) 🔝 8923113531 🔝 Cash Payment (COD) 👒
VIP Call Girls In Saharaganj ( Lucknow  ) 🔝 8923113531 🔝  Cash Payment (COD) 👒VIP Call Girls In Saharaganj ( Lucknow  ) 🔝 8923113531 🔝  Cash Payment (COD) 👒
VIP Call Girls In Saharaganj ( Lucknow ) 🔝 8923113531 🔝 Cash Payment (COD) 👒
 
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
 
Insurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usageInsurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usage
 
RSA Conference Exhibitor List 2024 - Exhibitors Data
RSA Conference Exhibitor List 2024 - Exhibitors DataRSA Conference Exhibitor List 2024 - Exhibitors Data
RSA Conference Exhibitor List 2024 - Exhibitors Data
 
Best Basmati Rice Manufacturers in India
Best Basmati Rice Manufacturers in IndiaBest Basmati Rice Manufacturers in India
Best Basmati Rice Manufacturers in India
 

Datamining 3rd Naivebayes

  • 1.
  • 2. 2
  • 3. 3
  • 4. 5.5.1 Theorem) n X = (T1 = x1 )∧(T2 = x2 )∧· · ·∧(Tn = xn ) CH X C = {C1 , C2 , ...} X CH X CH 5.3 P (CH ∩ X) P (X | CH )P (CH ) P (CH | X) = CH = (C = ) P (X) P (X) P (CH |A) A P (CH |X) 5.4 P (CH ∩ X) = P (CH | X)P (X) = PP (C| CHX) (CH ) 4 H ) (X H ∩ )P P (X|C
  • 5. P (CH ∩ X) P (X | CH )P (CH ) P (CH | X) = = P (X) P (X)
  • 6. P (C = | X) > P (C = × | X) P (C = | X) < P (C = × | X) 6
  • 7. P (X | C )P (C ) P (C | X) = P (X) P (C ) = N /N X = (T1 = x1 ) ∧ (T2 = x2 ) ∧ · · · ∧ (Tn = xn ) = x1 ∧ x2 ∧ · · · ∧ x3 P (X | C ) = P (x1 ∧ x2 ∧ · · · ∧ xn | C ) = P (x1 | C )P (x2 | C ) · · · P (xn | C ) n = P (xk | C ) k=1 P (X) CH
  • 8. P (C ) = 4/10 = 0.4, P (C× ) = 6/10 = 0.6 X = (T1 = No) ∧ (T2 = No) ∧ (T3 = Yes) ∧ (T4 = Yes) 8
  • 9. X = (T1 = No) ∧ (T2 = No) ∧ (T3 = Yes) ∧ (T4 = Yes) P (X | C ) = P (T1 = No | C ) × P (T2 = No | C ) ×P (T3 = Yes) | C ) × P (T4 = Yes | C ) P (T1 = No | C ) = 2/4 = 0.5 P (T2 = No | C ) = 0/4 = 0.0 P (T3 = Yes | C ) = 2/4 = 0.5 P (T4 = Yes | C ) = 0/4 = 0.0 P (X|C ) = 0.5 × 0.0 × 0.5 × 0.0 = 0.0 P (X | C ) · P (C ) = 0.0 × 0.4 = 0.0
  • 10. X = (T1 = No) ∧ (T2 = No) ∧ (T3 = Yes) ∧ (T4 = Yes) P (X | C× ) = P (T1 = No | C× ) × P (T2 = No | C× )× P (T3 = Yes | C× ) × P (T4 = Yes | C× ) P (T1 = No | C× ) = 4/6 = 0.667 P (T2 = No | C× ) = 4/6 = 0.667 P (T3 = Yes | C× ) = 1/6 = 0.167 P (T4 = Yes | C× ) = 4/6 = 0.667 P (X|C× ) = 0.667 × 0.667 × 0.167 × 0.667 = 0.0494 P (X | C× ) · P (C× ) = 0.0494 × 0.4 = 0.0198
  • 11. P (X | C ) · P (C ) = 0.0 P (X | C× ) · P (C× ) = 0.0198 P (X | C ) · P (C ) < (X | C× ) · P (C× ) 11
  • 12. 12