A classification tree was built to predict a dependent variable categorized as Yes, No. 80% of...

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A classification tree was built to predict a dependent variable categorized as "Yes", "No". 80% of the data set wereused to train the classification tree and the remaining 20% was used to test the resulting model. The predictionaccuracy was evaluated using the test set. The confusion matrix is below. Predicted Yes No (i) (ii) Yes 100 10 No 30 37 How many observations were used to train the model? How many ob- servations were used to test themodel? What is the accuracy, false-positive rate, and false-negative rate? As- sume that "Yes" is positive and "No" is negative. A classification tree was built to predict a dependent variable categorized as "Yes", "No". 80% of the data set wereused to train the classification tree and the remaining 20% was used to test the resulting model. The predictionaccuracy was evaluated using the test set. The confusion matrix is below. Predicted Yes No (i) (ii) Yes 100 10 No 30 37 How many observations were used to train the model? How many ob- servations were used to test themodel? What is the accuracy, false-positive rate, and false-negative rate? As- sume that "Yes" is positive and "No" is negative.

Answer rating: 100% (QA)

i Observations used for training and testing Training observations 80 of the dataset 14... View the full answer