The following is a training dataset that has ten one dimensional objects. Person id Home owner Refund
1 Yes TRUE
2 No TRUE
3 Yes FALSE
4 No FALSE
5 Yes FALSE
6 No TRUE
7 NO TRUE
8 NO FALSE
9 No FALSE
10 YES TRUE .
Build a decision stump (One level decision tree that split the root using entropy) for each bootstrap sample to predict if each object/person gets refund based on the maritał status homeowner attribute. C. Apply the decision stumps on the original dataset to predict a class label (refund) for each objects in the original data set. D. Determine the final prediction for each object using majority of votes. E. Find the training error of your model. *You should not use R for section B, C,D, and E -

In a heat transfer course, we can derive the equation for the temperature distribution in a flat rectangular plate. in this example, we will look at a plate at steadystate with three sides being held at t1, and one side held at t2. the temperature for any location on the plate, t(x, y), can be calculated by where create a function (prob3_5) that will take inputs of vectors x and y in feet, scalar n, scalars l and w in feet and scalars t1 and t2 in degrees fahrenheit. it will output a matrix t which is the temperature of each x and y locations. t will have the number of columns equal to the number of elements in x and rows equal to the number of elements in y. though this can be done without loops (perhaps more efficiently), your program must use a nested loop.