Linear Regression Model with M5 Method of Attribute Selection: FAULTS = - 0.0516 * NUMUORS + 0.0341 * NUMUANDS - 0.0027 * TOTOTORS - 0.0372 * VG + 0.2119 * NLOGIC + 0.0018 * LOC + 0.005 * ELOC - 0.3091
Linear Regression Model with Greedy Method of Attribute Selection:
FAULTS = - 0.0482 * NUMUORS + 0.0336 * NUMUANDS - 0.0021 * TOTOTORS - 0.0337 * VG + 0.2088 * NLOGIC + 0.0019 * LOC - 0.3255
Obtain the predictions for both the fit data set and the test data set using the above two models. Perform Module Order Modeling for both fit and test data sets using both regression modelsCompare the performances of MOM for both linear regression models. Use Alberg Diagram and Peformance Curve for each Model using fit and test data setsUse tables to summarize the results of MOM. Also provide analysis of your summary.

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Write a car class having two private member variables called tank and speed. write public methods called pumpgas and gofast. the method pumpgas gets an integer for gas that must be pumped. that value needs to be added to tank (no more than 20 gallons). it must return the amount of gas that is purchased ($4 per gallon). the method gofast should increase the speed by 5 each time it is called. write a constructor for the above class that initialized both variables to zero. write a tostring to display both the tank and speed when the car is printed. modify the car class to implement the interface comparable and an interface called carinter having the public methods in carinter. write the main program to create an array of size 5 of type car. create 5 car objects having each location of the array to refer to one of the cars. test the pumpgas, gofast, equals method on the array items. write an enhanced loop to print all the car values (using a tostring written last time).write a generic method to find the minimum of four items. pass int, double, char, string and car objects to test this method.