Instead of predicting duration directly, an alternative is to predict the average speed of the taxi ride using linear regression, then compute an estimate of the duration from the predicted speed and observed distance for each ride. Assign speed_rmse to the root mean squared error in the duration predicted by a model that first predicts speed as a linear combination of features from the design_matrix function, fitted on the training set, then calculates duration from the predicted speed and observed distance.

What is the output of the following program? #include using namespace std; class bclass { public: void print() const; bclass(int a = 0, int b = 0); //postcondition: x = a; y = b; private: int x; int y; }; class dclass: public bclass { public: void print() const; dclass(int a = 0, int b = 0, int c = 0); //postcondition: x = a; y = b; z = c; private: int z; }; int main() { bclass bobject(2, 3); dclass dobject(3, 5, 8); bobject.print(); cout < < endl; dobject.print(); cout < < endl; return 0 ; } void bclass: : print() const { cout < < x < < " " < < y < < endl; } bclass: : bclass(int a, int b) { x = a; y = b; } void dclass: : print() const { bclass: print(); cout < < " " < < z < < endl; } dclass: : dclass(int a, int b, int c) : bclass(a, b) { z = c; }