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presentation slides - INFORMS NY

presentation slides - INFORMS NY

Logistic Vs. trees.

Logistic Vs. trees. Titanic No missing Values.Consistency InformationCharacteristic'informs.titanic_no_missing' # obs.Number of variablesNumber of continuous variablesNumber of class variablesValue756734Models * Vars * CoeffsEstimate Pr > Chi-Square Importance # RulesVar Sel Type VariableForestAGE0.546 493PASSCLASS1 0.400 66PASSCLASS2 0.248 58PASSCLASS3 0.465 59SEX 1.000 97STEPWISE AGE -0.039177938 0.000PASSCLASS1 1.2919799232 0.000PASSCLASS3 -1.229467857 0.000SEX 2.631357225 0.000Intercept -0.163634963 0.550Trees AGE 0.408 1PASSCLASS3 0.556 2SEX 1.000 1Trees used 3, Forest 5, while Stepwise 5 plus the intercept.— 34 —

Training: Rates '-' ==> misclass &MissprecSurvived = 1Model Name_PREDICTED0 1 OverallClassRatePrecRateClassRatePrecRateClassRatePrec Rate0 FOREST96.39 78.35 -3.61 -7.58 96.39LOGISTIC_STEPWISE 83.97 80.35 -16.03 -24.23 83.971TREES 96.61 77.12 -3.39 -7.46 96.61FOREST -37.70 -21.65 62.30 92.42 62.30LOGISTIC_STEPWISE -29.07 -19.65 70.93 75.77 70.93OverallTREES -40.58 -22.88 59.42 92.54 59.42FOREST 78.35 92.42 82.28 82.28LOGISTIC_STEPWISE 80.35 75.77 78.57 78.57TREES 77.12 92.54 81.22 81.22Trees have the highest classification rate (96.39%)and an excellent precision rate (92.42%). Forestcomes at a close second.— 35 —

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