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SEO Keyword summary for www.slideshare.net/fmuntaha/an-introduction-to-decision-trees-43694869
Keywords are extracted from the main content of your website and are the primary indicator of the words this page could rank for. By frequenty count we expect your focus keyword to be decision
Focus keyword
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Short Tail Keywords decision tree box |
long Tail Keywords (2 words) box office decision tree decision making decision trees small box |
long Tail Keywords (3 words) large box office medium box office small box office box office large office large box office medium box box office medium |
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introduction decision trees ppt
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introduction decision trees download pdf view online free
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slideshare scribd company logo introductiontodecision treeswidely used quantitative techniqueto make decision duration minutespresented byfahim muntaha objective learning outcome have clear idea about tree how use tool making quantitativedecisions impacts these decisions real life content define steps trees limitations advantage decisionmaking processproblem decisionquantitative analysislogichistorical datamarketing researchscientific analysismodelingqualitative analysisemotionsintuitionpersonal experienceand motivationrumors tools payback analysis simulations visual representation choicesconsequences probabilities andopportunities way breaking down complicatedsituations easiertounderstandscenarios applying logic likely decisiondecision notation box show choice themanager has node circle probabilityoutcome will occur chance lines connect outcomes choiceor probability terminal nodes represented triangles optional easy example two choicesgo graduate school toget master csgo work realworld treedecisionnodechancenode event solving involves pruning all but thebest findingexpected values possible states ofnature works like flow chart paths mutually exclusive marys factorymary ceo gadget factoryshe wondering whether not good expandher factory year cost expand her mif she expands expects receive ifeconomy people continue buy lots gadgetsand economy badif does nothing stays revenue only badshe also assumes there goodeconomy bad exampleexpand factorycost mdont economyprofit mgood profit mbad mevexpand mevno therefore you should area draw diagramb datai payoff table probabilityii under uncertaintyiii expected returniv value perfectinformation problem jenny lindjenny lind writer romance novelsa movie network bothwant rights one morepopular signs thenetwork single lumpsum moviecompany amount receivedepends market response hermovie step decisiontreesmall officemedium officelarge officesmall officesign cosign payouts small office medium large payout flat rate psmall pmedium plarge quantitativedata treepayoffsmall underuncertainty criteria maximax choose alternative thatmaximizes maximum forevery optimistic criterion maximin minimum pessimistic withthe highest treesmall ererer payoffdecisionsstates nature payoffsmallboxofficemediumboxofficelargeboxofficesmallboxofficemediumboxofficelargeboxofficesign withmoviecompany sign withtvnetwork priorprobabilities equationprobability payoffev iiin number naturevalue payoffalternative iii return criteriaevmovie evbestevtv select largest valueevtherefore using themovie contract solvedsmall something rememberjennys going made onetime earn either moviecontract calculated nevertheless useful maximize jennys expectedreturns long run continues thisapproach evpi places upper bound whatone would pay additional informationevpi tolearn future undercertainty evuc minus maximumevevpi perfect informationevpiwhat most shouldbe willing learn sizeof before shedecides whom calculationevwpi evc evbest evmovie from previous spend information beforemaking scientific aids structure solve sequential problems especially beneficial complexity theproblem grows operational encourage thinking planning local optimal solution global possibility duplication same subtreeon different spurious relationships thanks managing uncertainties theory chapter risk ppt introduction transportation data envelopment logistic regression condition uncertainty operation research complete note sood uncertaionity cima edition decisiontrees women science meetup atx mba decsci decison cascading predictive modeling machine lecture powerpoint presentation templates andrew moore chosen our business press firm whose name individual becdoms better causal reasoning systems fundamentals makingdocx digital marketing roi blogworld nyc week simulation msbapptx sos session midterm being right starts knowing youre wrong accurate campaign targeting classification algorithms journal ledger trial balance time money know imc integratedmarketingcampaignstinhat phpapp management managerial tasks skills positioning segmentation consumer channeldifferentiationimagedifferentiation plan kotler ippt verified paypal account google star reviews valuation webinar series tuesday june final presentationpptx auditing study material bcom students understanding user needs satisfying them bemetals investor presentationjune pdf premium mean stack development solutions modern businesses helen lubchak miltech agency managed advisory board career path defining adanihindenburg case sebi investigatingpptx project file report bba semesterpdf cryptocurrency investment platform binance savings authentically social presented corey perlman top mailing list providers usapptx sustainability balancing environment equity discover innovative creative projects highlight journey throu hamster kombat telegram game surpasses million playerstoken release sch observation lab assignment tem modelingmarketingstrategiesmkscollumbiauniversitypdf ting anh success unit hsdoc
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