Suppose that we apply bagging and random forests respectively to Weekly data set (the same one used in the assignment) and obtain two models M1 and M2. Given the codes as below, which model is from bagging and which one is from random forests? In general, which model (M1 or M2) is more likely to have a higher prediction accuracy on the new testing data set? M1 <- randomForest (Direction M2 <- random Forest (Direction ., data = Weekly, mtry - 2) ., data = Weekly, mtry = 6)

problem:

f(x) = - 2/3x + 5; f(5/2)

substitute 5/2 in for x:

f(5/2) = -2/3(5/2) + 5

simplify the right side:

f(5/2) = -5/3 + 5

f(5/2) = 10/3

f(5/2) = 10/3

i hope this !

the simplified form is

step-by-step explanation:

we want to simplify the expression,

let us change the middle bar to a normal division sign to obtain,

we multiply the first fraction by the reciprocal of the second fraction to obtain,

we factor to obtain,

we cancel the common factors to get,

we simplify to get,

the correct answer is b

mean, 40.6

step-by-step explanation: