1 / 8

data science course

ExcelR offers End-to-End training to placement services on data science Training in Bangalore. Internship & Certification is provided to FRESHERS / STUDENTS. ExcelR is one of the BEST data science TRAINING INSTITUTES in INDIA. Top most Data Science faculty from IIT with 20 years of professional experience.

marketer
Télécharger la présentation

data science course

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Advanced Regression Nega)ve Binomial Poisson Regression ZeroInflated Mul)nomial Regression AGENDA © 2013 ExcelR Solutions. All Rights Reserved

  2. Multinomial Regression •  Logis'cregression(Binomialdistribu'on)isusedwhenoutputhas‘2’categories •  Mul'nomialregression(classifica'onmodel)isusedwhenoutputhas>‘2’categories •  Extensiontologis'cregression •  Nonaturalorderingofcategories Modeof transport Car Carpool Bus Rail Allmodes Count 218 32 81 122 453 •  Responsevariablehas>‘2’categories&henceweapplymul'logit Probability 0.48 0.07 0.18 0.27 1 •  Understandtheimpactofcost&'meonthevariousmodesoftransport © 2013 ExcelR Solutions. All Rights Reserved

  3. Multinomial Regression Whetherwehave‘Y’(response)or‘X’(predictor),whichiscategoricalwith‘s’categories ü  Lowestinnumerical/lexicographicalvalueischosenasbaseline/reference ü  Missinglevelinoutputisbaselinelevel ü  Wecanchoosethebaselinelevelofourchoicebasedon‘relevel’func'oninR ü  Modelformulatestherela'onshipbetweentransformed(logit)Y&numericalXlinearly ü  Modelingquan'ta'vevariableslinearlymightnotalwaysbecorrect •  © 2013 ExcelR Solutions. All Rights Reserved

  4. Multinomial Regression - Output Itera'onHistory: •  Itera'veprocedureisusedtocomputemaximumlikelihoodes'mates •  #itera'ons&convergencestatusisprovided •  -2logL=2*nega'veloglikelihood •  -2logLhasχ2distribu'on,whichisusedforhypothesistes'ngofgoodnessoffit #parameters=27 © 2013 ExcelR Solutions. All Rights Reserved

  5. Multinomial Regression - Output ‘car’hasbeenchosenasbaseline •  x=vectorrepresen'ngthevaluesofallinputs •  Log(P(choice=carpool|x)/P(choice=car|x)=β20+β21*cost.car+β22*cost.carpool+……………. Thisequa'oncomparesthelogofprobabili'esofcarpooltocar Theregressioncoefficient0.636indicatesthatfora‘1’unitincreasesthe‘cost.car’,thelogoddsof‘carpool’to‘car’ increasesby0.636 •  Interceptvaluedoesnotmeananythinginthiscontext •  •  IfwehaveacategoricalXalso,sayGender(female=0,male=1),thenregressioncoefficient(say0.22)indicates thatrela'vetofemales,malesincreasethelogoddsof‘carpool’to‘car’by0.22 © 2013 ExcelR Solutions. All Rights Reserved

  6. Probability •  Letp=p(x|A)betheprobabilityofanyevent(sayairi'on)undercondi'onA(say gender=female) Odds •  Thenp(x|A)÷(1-p(x|A)iscalledtheoddsassociatedwiththeevent Odds Ratio •  Iftherearetwocondi'onsA(gender=female)&B(gender=male)thenthera'o p(x|A)÷(1-p(x|A)/p(x|B)÷(1-p(x|B)iscalledasoddsra'oofAwithrespecttoB Relative Risk •  p(x|A)÷p(x|B)iscalledasrela'verisk hips://en.wikipedia.org/wiki/Rela've_risk © 2013 ExcelR Solutions. All Rights Reserved

  7. Odds Ratio •  Oddsra'oiscomputedfromthecoefficientsinthelinearmodelequa'onbysimply exponen'a'ng •  Exponen'atedregressioncoefficientsareoddsra'oforaunitchangeinapredictor variable •  Theoddsra'oforaunitincreaseincost.caris1.88forchoosingcarpoolvscar © 2013 ExcelR Solutions. All Rights Reserved

  8. Goodness of fit Linear AnalysisofVariance ResidualDeviance OLS GLM AnalysisofDeviance ResidualSumofSquares MaximumLikelihood •  ResidualDevianceis-2logL •  AddingmoreparameterstothemodelwillreduceResidualDevianceevenifitisnot goingtobeusefulforpredic'on •  Inordertocontrolthis,penaltyof“2*numberofparameters”isaddedtoto Residualdeviance •  Thispenalizedvalueof-2logLiscalledasAICcriterion •  AIC=-2logL+2*numberofparameters Note:“Mul'logitModelwithInterac(on” © 2013 ExcelR Solutions. All Rights Reserved

More Related