manual solution

1. A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspectivc) than ir faise afgative resuit. The models shnu'd be evaluated based on the following criteria: i. Must have a recall rate of at least 80%, ii. Must have a false positive rate of 10% or less, iii. Must minimize business costs. After creating each binary classification model, the data scientist generates the corresponding confusion matrix. Which confusion matrix represents the model that satisfies the requirements? Justify your answer? (a) TN=91,FP=9,FN=22,TP=78. (b) TN=99,FP=1,FN=21,TP=79. (c) TN=96,FP=4, FN=10,TP=90. (d) TN=98,FP=2,FN=18,TP=82 ? 2. Leave-one out cross validation generally gives less accurate estimates of true test error than 10-fold cross validation. Suppose we want to compute 10-Fold Cross-Validation error on 100 training examples. We need to compute crror N1 times, and the Cross-Validation error is the average of the errors. To compute each error, we need to build a model with data of size N2, and test the model on the data of size N3. What are the appropriate numbers for N1,N2 and N3. (a) N1=10,N2=90,N3=10. (b) N1=1,N2=90,N3=10. (c) N1=10,N2=100,N3=10. (d) N1=10,N2=100,N3=100. 3. Explain Apriori Property with example