Cоnsider the Autо dаtаset cоnsisting of 392 observаtions on 9 variables Mpg: miles per gallonCylinders: Number of cylinders between 4 and 8Displacement: Engine displacement (cu. inches)Horsepower: Engine horsepowerWeight: Vehicle weight (lbs.)Acceleration: Time to accelerate from 0 to 60 mph (sec.)Year: Model year (modulo 100) Origin: Origin of car (1. American, 2. European, 3. Japanese) Name: Vehicle nameMpg01: 1 if mpg above median mpg, 0 otherwise We wish to predict whether a given car gets high or low gas mileage (mpg01). We used LDA on train data to predict mpg01. R output is provided below.> lda.fitCall:lda(mpg01 ~ cylinders + displacement + weight, data = Auto.train) Prior probabilities of groups: 0 10.5068027 0.4931973 Group means: cylinders displacement weight0 6.637584 266.1946 3588.7321 4.213793 118.0552 2358.386 Coefficients of linear discriminants: LD1cylinders -0.371188396displacement -0.000695555weight -0.001015639 > lda.predict = predict( lda.fit, newdata=Auto.test )> CM = table( predicted=lda.predict$class, truth=Auto.test$mpg01 )> print( CM ) truthpredicted 0 1 0 42 1 1 5 50Report the confusion matrix and calculate the sensitivity and specificity of the classifier (show work). Answers without supporting calculations will receive no credit.Using the Math Editor in Blackboard UltraWhen you need to show a calculation or mathematical expression:Click in the answer box.Select the + (Add Content) button in the editor.Select Math to open the Math Editor.Enter your equation or calculation and select Insert.Continue typing your explanation in the answer box. confusion matrix: sensitivity=specificity=
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