代做Assignment 3 Big Data and Machine Learning for Economics and Finance代做R语言

Assignment 3

Big Data and Machine Learning for Economics and Finance

Submission Rules:

 Provide an html document that is generated by RMarkdown and that con- tains

         the R code,

         the R output,

         and your comments on the output.

Comment each line of your R code as well. Give thorough explanations throughout.

 Please note that the function set.seed() may not be used at any time in the assignment.

 Please note that, when providing your answers, you may not use any extra packages other than the ones explicitly mentioned in each exercise. For example, if the question says ''the only extra packages allowed are ISLR2 and boot'', then you may type library(ISLR2), and library(boot) when writing your answers to the questions in that exercise, but you may not type library(MASS) or library(any other package) anywhere in your submission.

 When asked to carry out a certain task (such as, for example, fitting a certain model or running a certain algorithm), it must be determined first whether that task is feasible or not, and when feasible, whether it can be carried out exactly as prescribed in the question or whether it can only be approximately carried out.

Exercise 1. (40 points) The only extra packages allowed in this exrecise are tree and boot. Consider the following data generating mechanism: X is a uniform random variable on the

interval [0;100] and

Y=1{30>X+U}+1{X+U>90}

where U is a standard normal random variable that is independent of X .

Assuming that X is the input variable and Y is the output variable, we are interested in comparing predictions from classification trees with ones based on Logistic Regressions.

1.  Generate a sample of size n=1000 from that model.

a.  Using R, produce a scatterplot of X vs. Y.

b.  Produce another plot representing the different observations of X , where each of the observations is given a different color depending on the value of Y.  Are the colors separable using a hyperplane?

c.  We are interested in  giving predictions for Y when x=10, 50 or 90 using a clas- sification tree. After fitting a tree to the data, show how to give predictions both using the function predict, and by arguing based on a graphical representation of the tree.

d.  Run logistic regression and give predictions for the same 3 values of x.

e.  Compare the prediction performance the two methods.

2.  Attempt to reproduce the results in the following figure using R.

Figure 1. Monte Carlo Experiment

3.  Based on your knowledge, examine all classification methods  learned in this course and establish which methods would perform well on samples drawn using the data generating mechanism described in this exercise.

Make a table where on the left you write down the name of the method(s), and on the right you explain if you believe it would perform well while justifying your answer. Your answer and justification for each method should not be more than 10 words long. Please note that this part of the exercise should not be answered with any R coding.

Exercise 2. (30 points) The only extra packages allowed in this exrecise are tree and boot. An applied data analyst is interested in assessing the performance of supervised learning

when applied to the following data generation scheme

Z=Y2+U

X =exp (Z)+V

where Y , U and V are independent standard normal random variables.

1.  Generate a sample of size n=10000 from that model.  Assuming that Y is the input variable and X is the output variable, we are interested in comparing CART and a Generalized Linear Model.

a.  Construct a tree and show how to use it in order to make a prediction for X when y=1.  Use both the predict function and a plot of the tree to make the prediction.

b.  Run  a  generalized  linear  model  and  use  it to  give  a prediction  for X corre- sponding to the same value of the input variable as the previous question.

c.  Compare the prediction performance of the two methods.

2.  Another applied data analyst looks at a sample of size n generated from the same data generation scheme and concludes that a supervised learning prediction exercise does not make sense as the X and Y variables are seemingly uncorrelated.

a.  Using the bootstrap, show whether the applied data analyst is correct in their conclusion regarding the correlation.

b.  Do you believe that the applied data analyst is right in believing that a super- vised learning exercise does not make sense in this particular case?

3.  Based on your  knowledge, examine  all  supervised  learning  methods  learned  in  this course and establish which methods would perform well on samples drawn using the data generation scheme described in this exercise.

Make a table where on the left you write down the name of the method(s), and on the right you explain if you believe it would perform well while justifying your answer. Your answer and justification for each method should not be more than 10 words long.

Please note that this part of the exericse should not be answered with any R coding. Exercise 3. (30 points) No extra packages are allowed in this exrecise.

1.  Consider the following dendrogram:

Figure 2. 10 observations

We are interested in clustering the data into three groups. What are the three groups obtained from the dendrogram?  Which group contains the closest two points in the sample? Is the dendrogram well balanced?

2.  Consider the following scatter plot representing the data on two variables X1  and X2 :

Figure 3. 4 observations

Going from left to right, the same 4 observations have the following values for a third variable Y=(1;2;3;0).

a.  If Y is considered as the output variable in a supervised learning setting and (X1 ;X2) are considered the input variables, would this be a classification or a regression task?

b.  If we were to fit a single split tree stump to this dataset, how many possible configurations are there?

c.  Construct the optimal tree stump.




热门主题

课程名

mktg2509 csci 2600 38170 lng302 csse3010 phas3226 77938 arch1162 engn4536/engn6536 acx5903 comp151101 phl245 cse12 comp9312 stat3016/6016 phas0038 comp2140 6qqmb312 xjco3011 rest0005 ematm0051 5qqmn219 lubs5062m eee8155 cege0100 eap033 artd1109 mat246 etc3430 ecmm462 mis102 inft6800 ddes9903 comp6521 comp9517 comp3331/9331 comp4337 comp6008 comp9414 bu.231.790.81 man00150m csb352h math1041 eengm4100 isys1002 08 6057cem mktg3504 mthm036 mtrx1701 mth3241 eeee3086 cmp-7038b cmp-7000a ints4010 econ2151 infs5710 fins5516 fin3309 fins5510 gsoe9340 math2007 math2036 soee5010 mark3088 infs3605 elec9714 comp2271 ma214 comp2211 infs3604 600426 sit254 acct3091 bbt405 msin0116 com107/com113 mark5826 sit120 comp9021 eco2101 eeen40700 cs253 ece3114 ecmm447 chns3000 math377 itd102 comp9444 comp(2041|9044) econ0060 econ7230 mgt001371 ecs-323 cs6250 mgdi60012 mdia2012 comm221001 comm5000 ma1008 engl642 econ241 com333 math367 mis201 nbs-7041x meek16104 econ2003 comm1190 mbas902 comp-1027 dpst1091 comp7315 eppd1033 m06 ee3025 msci231 bb113/bbs1063 fc709 comp3425 comp9417 econ42915 cb9101 math1102e chme0017 fc307 mkt60104 5522usst litr1-uc6201.200 ee1102 cosc2803 math39512 omp9727 int2067/int5051 bsb151 mgt253 fc021 babs2202 mis2002s phya21 18-213 cege0012 mdia1002 math38032 mech5125 07 cisc102 mgx3110 cs240 11175 fin3020s eco3420 ictten622 comp9727 cpt111 de114102d mgm320h5s bafi1019 math21112 efim20036 mn-3503 fins5568 110.807 bcpm000028 info6030 bma0092 bcpm0054 math20212 ce335 cs365 cenv6141 ftec5580 math2010 ec3450 comm1170 ecmt1010 csci-ua.0480-003 econ12-200 ib3960 ectb60h3f cs247—assignment tk3163 ics3u ib3j80 comp20008 comp9334 eppd1063 acct2343 cct109 isys1055/3412 math350-real math2014 eec180 stat141b econ2101 msinm014/msing014/msing014b fit2004 comp643 bu1002 cm2030
联系我们
EMail: 99515681@qq.com
QQ: 99515681
留学生作业帮-留学生的知心伴侣!
工作时间:08:00-21:00
python代写
微信客服:codinghelp
站长地图