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Amelia_​Fairy

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P values > 0.05 ID, hearing(left), cholesterol, but accuracy drops for 0.75 if idelete these acc 0.71removing outliers: acc 0.74 recall 0.69removing duplicates acc 0.73 recall 0.66 Highest F, gender, hemoglobin, height, weight, trigl, gtp, serum, waist, hdl,age, it also drop 0.01 in acc, recall (correctly identify smoker - 1) 0.69removing outliers: 0.74 in acc and recall 0.69 or 0.71 acc and recall 0.92 Node 1Node 13Node 25Node 26Node 27Node 28Node 29Node 30Node 34Node 35Node 36Node 38Node 39Node 84Node 85Node 86gendertartarNode 91Node 92 CSV Reader Joiner Amelia_Preprocessing Amelia_ModelTraining Amelia_ModelTraining3 Amelia_ModelTraining2 Weka Predictor(3.7) Weka Predictor(3.7) Weka Predictor(3.7) Scorer Column Filter Row Filter Scorer Column Filter Row Filter Amelia_ModelEvaluation Concatenate Concatenate Partitioning One-way ANOVA Column Filter Rule Engine Rule Engine Amelia_FeatureSelection ROC Curve(JavaScript) ROC Curve(JavaScript) P values > 0.05 ID, hearing(left), cholesterol, but accuracy drops for 0.75 if idelete these acc 0.71removing outliers: acc 0.74 recall 0.69removing duplicates acc 0.73 recall 0.66 Highest F, gender, hemoglobin, height, weight, trigl, gtp, serum, waist, hdl,age, it also drop 0.01 in acc, recall (correctly identify smoker - 1) 0.69removing outliers: 0.74 in acc and recall 0.69 or 0.71 acc and recall 0.92 Node 1Node 13Node 25Node 26Node 27Node 28Node 29Node 30Node 34Node 35Node 36Node 38Node 39Node 84Node 85Node 86gendertartarNode 91Node 92CSV Reader Joiner Amelia_Preprocessing Amelia_ModelTraining Amelia_ModelTraining3 Amelia_ModelTraining2 Weka Predictor(3.7) Weka Predictor(3.7) Weka Predictor(3.7) Scorer Column Filter Row Filter Scorer Column Filter Row Filter Amelia_ModelEvaluation Concatenate Concatenate Partitioning One-way ANOVA Column Filter Rule Engine Rule Engine Amelia_FeatureSelection ROC Curve(JavaScript) ROC Curve(JavaScript)

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