Suppose we obtain n labeled samples {(xi , yi)} n i=1 from our underlying distribution d. suppose we break this into ntrain and ntest samples for our training and test set. recall our definition of the true least squares error (f) = e(x, y)∼d[(f(x) − y) 2 ] (the subscript (x, y) ∼ d makes clear that our input-output pairs are sampled according to d). our training and test losses are defined as: btrain(f) = 1 ntrain x (x, y)∈training set (f(x) − y) 2 btest(f) = 1 ntest x (x, y)∈test set (f(x) − y)

When creating a budget, log fixed expenses before income. after income. after savings. at the top.

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Consider the following statements: struct nametype{string first; string last; }; struct coursetype{string name; int callnum; int credits; char grade; }; struct studenttype{nametype name; double gpa; coursetype course; }; studenttype student; studenttype classlist[100]; coursetype course; nametype name; mark the following statements as valid or invalid. if a statement is invalid, explain why.a.) student.course.callnum = "csc230"; b.) cin > > student.name; c.) classlist[0] = name; d.) classlist[1].gpa = 3.45; e.) name = classlist[15].name; f.) student.name = name; g.) cout < < classlist[10] < < endl; h.) for (int j = 0; j < 100; j++)classlist[j].name = name; i.) classlist.course.credits = 3; j.) course = studenttype.course;