Machine Learning and Big Data for Economics and Finance - Finance Assessment Answer

Finance Assessment Task

Consider the two variables in the dataset Assign3.csv. We are interested in predicting the second variable Y given the rst variable X. 1. Fit a linear regression model to the data. Show the data scatter plot on the same gure with the values predicted by the linear model. 2. Fit a quadratic regression model to the data. Show the data scatter plot on the same gure with the values predicted by the quadratic model.. 3. We are interested in constructing a step function learner as follows: First draw a random number U uniformly on the interval spanned by the minimum and maximum values of the inputs (x1; :::; xn) and then use it to construct the following function whose purpose is to  ive the prediction of Y given X = x:

f(x) = 1I(U 6 x) + 2I(U > x); where
1 and 2 are just unknown constants to be learned. It goes without saying that I(some statement) is the indicator function that equals 1 when the statement is true and 0 otherwise. a. Use two dierent methods to compute the estimate f^(x) =^1I(U 6x) + ^2I(U > x). Is f^ a strong learner?
b. Use one of the previous two methods to write an R function that takes as input x and the data (x1;:::;xn; y1;:::; yn) and gives as output f^(x). Make sure the function is capable of dealing with the case where x conatains more than one number. c. Using three dierent runs of the previous function, create three different plots where, on each, f^ is shown together with the scatter plot of the data. 4. Write an R function that applies boosting to the previous step function learner. That R function should take as inputs: the data, B the number of boosting iterations,  the learning rate and an optional argument indicating the size of the test subsample in case a validation set approach is needed. As output the function should give: f^ boost the boosted learner evaluated at the training data and the training mean squared error evaluated for each iteration b=1;:::;B of the boosting algorithm. Also, in case the size of the test subsample is greater than zero, the function should output: f^boost evaluated at the test sample and the test MSE evaluated for each iteration b =1; :::; B. a. Use that function to plot f^ boost on top of the data scatter plot for=0.01 and for B =10000. Show the same with dierent

This Finance Assessment has been solved by our Finance experts at My Uni Paper. Our Assignment Writing Experts are efficient to provide a fresh solution to this question. We are serving more than 10000+ Students in Australia, UK & US by helping them to score HD in their academics. Our Experts are well trained to follow all marking rubrics & referencing style.

Be it a used or new solution, the quality of the work submitted by our assignment experts remains unhampered. You may continue to expect the same or even better quality with the used and new assignment solution files respectively. There’s one thing to be noticed that you could choose one between the two and acquire an HD either way. You could choose a new assignment solution file to get yourself an exclusive, plagiarism (with free Turnitin file), expert quality assignment or order an old solution file that was considered worthy of the highest distinction.