STK2100 - Maskinl?ring og statistiske metoder for prediksjon og klassifikasjon

Weekly exercices

Here you find the weekly exercises for the coming week, as well as an overview of exercises for past weeks. The week number refers to when the exercises will be discussed in the exercise class.

To learn the material well, it is important to spend time and make real efforts on trying to solve the exercises, preferably before the exercise class.

Some of the exercises are from the book James et al 2013: An Introduction to Statistical Learning, and will be referred to as ISLR

Note that the link to book website given in the book does not work any more; the new link is https://hastie.su.domains/ElemStatLearn/.

A link to the extra exercises is here.

Exercises the coming week

Week 19

Exam 2021: Problems 1, 2 and 3.

Modified version of extra exercise 10: The purpose in this exercise is to reproduce Figures 8.6 and 8.8 of ISLR. The data set to be used in the analysis is the Heart data, store in the file Heart.csv in the data folder of the course. Here are the modified exercises:

a) Do as written in the exercise.

b) Reproduce Figure 8.6 of ISLR, but use r-code-week11.R from the lecture on March 25 as inspiration.

c) Do as written in the exercise, but to get the black curve, use mtry=p (i.e. do splits based on all covariates).

d) Do as written in the exercise, and additionally, you may also use r-code-week14.R from the lecture on April 9 as inspiration.

e) Add addtional curves to the plot, by also fitting gradient boosted trees, with possibly different tree sizes, using r-code-week13.R from the lecture on April 8 as inspiration. Here, you only need to compute the test error.

Solution: extra_10.r

Exercises for past weeks

Week 18

Exercises from the book: 9.6 (in b only fit a tree). The ozone data are available here with some info here

Solutions: esl_9_6.r

Exercises from ISLR: Exercise 8.4, 8.8a-c,e

Solutions: islr_8_4.pdf and code examples in Chapter 8.3 Lab: Decision Trees in ISLR or https://rpubs.com/ppaquay/65564 (In 8.8b, use "text(tree_model, pretty = 0, cex = 0.7)" for a nicer plot).

Exam 2019: Problem 2.

Solution: /studier/emner/matnat/math/STK2100/oppgaver

Week 15

Extra exercises 12 and 13. You find the zip_nn.R file with examples on the use of nnet() and mlp() on the 2022 page: /studier/emner/matnat/math/STK2100/v22/r-scripts/.

Solutions: extra_12.r and extra_13.r.

Week 14

Exam STK2100 2019: Exercises 1a,d,e and 3

Exam STK2100 2022: Exercise 1 (in d you do not need to consider the three last methods in the table)

Solutions: /studier/emner/matnat/math/STK2100/oppgaver

Week 13

Exercises from the book: 5.4 and 5.7

Solutions: this solution manual, and/or Vera's notes: esl_5_4.pdf esl_5_7.pdf.

Exercises from ISLR: Exercise 7.1, 7.9

Solutions: https://rpubs.com/lmorgan95/ISLR_CH7_Solutions. Note that there are some similarities between ISLR 7.1 and ESL Ex. 5.1 from week 11 and R-code-examples in ISLR Ch. 7.8 Lab: Non-linear Modeling.

Extra exercise 7

Solutions: Vinnie's solution, pp. 22-27.

Week 11

Exercises from the book: 4.2 and 5.1

Solutions: See this solution manual, on which Vera's notes from 2024 is based (esl_4_2a.pdf, esl_5_1.pdf).

Exercises from ISLR: 4.9 and 4.14 (a-f)

Solutions: islr2_4_9.pdf and Chapter 4.7 Lab: Classification Methods in the ISLR book for R-code-examples similar to 4.14.

Exam STK2100 2018: Problem 2

Solution: /studier/emner/matnat/math/STK2100/oppgaver/STK2100_2018_fasit.pdf (in Norwegian).

Extra exercise 6

Solutions: See