library(pacman)
p_load(tidyverse, janitor, naniar, DataExplorer, Boruta, mlbench)Stat. 652: Final
Answer the following questions:
Question 1
Read the paper, “Feature Selection with the Boruta Package” by Miron B. Kursa and Witold R. Rudnicki. See the Final R Project subdirectory /Boruta for the pdf of the Journal of Statistical Software paper from 2010.
What are the important variables identified by the Boruta algorithm from the Ozone data? Use the output of the provided code below.
Answer:
Code:
data(Ozone)
head(Ozone) V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
1 1 1 4 3 5480 8 20 NA NA 5000 -15 30.56 200
2 1 2 5 3 5660 6 NA 38 NA NA -14 NA 300
3 1 3 6 3 5710 4 28 40 NA 2693 -25 47.66 250
4 1 4 7 5 5700 3 37 45 NA 590 -24 55.04 100
5 1 5 1 5 5760 3 51 54 45.32 1450 25 57.02 60
6 1 6 2 6 5720 4 69 35 49.64 1568 15 53.78 60Note that the target variable is V4 = Daily maximum one-hour-average ozone reading.
Convert V1, V2, and V3 to integers.
Ozone <- Ozone %>% mutate(
V1 = as.integer(V1),
V2 = as.integer(V2),
V3 = as.integer(V3)
)
head(Ozone) V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
1 1 1 4 3 5480 8 20 NA NA 5000 -15 30.56 200
2 1 2 5 3 5660 6 NA 38 NA NA -14 NA 300
3 1 3 6 3 5710 4 28 40 NA 2693 -25 47.66 250
4 1 4 7 5 5700 3 37 45 NA 590 -24 55.04 100
5 1 5 1 5 5760 3 51 54 45.32 1450 25 57.02 60
6 1 6 2 6 5720 4 69 35 49.64 1568 15 53.78 60Ozone2 <- Ozone %>% clean_names()
head(Ozone2) v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13
1 1 1 4 3 5480 8 20 NA NA 5000 -15 30.56 200
2 1 2 5 3 5660 6 NA 38 NA NA -14 NA 300
3 1 3 6 3 5710 4 28 40 NA 2693 -25 47.66 250
4 1 4 7 5 5700 3 37 45 NA 590 -24 55.04 100
5 1 5 1 5 5760 3 51 54 45.32 1450 25 57.02 60
6 1 6 2 6 5720 4 69 35 49.64 1568 15 53.78 60It is always a good idea to check for duplicate records/examples/rows in your dataset.
get_dupes(Ozone2)No variable names specified - using all columns.No duplicate combinations found of: v1, v2, v3, v4, v5, v6, v7, v8, v9, ... and 4 other variables [1] v1 v2 v3 v4 v5 v6
[7] v7 v8 v9 v10 v11 v12
[13] v13 dupe_count
<0 rows> (or 0-length row.names)Start by investigating the missing values and completeness of the features in the data. Note that the age variable contains some missing values.
vis_miss(Ozone2)
gg_miss_var(Ozone2)
gg_miss_var(Ozone2, show_pct = TRUE)
Do not run this code when Rendering the .qmd file.
create_report(Ozone2, y = "v4", output_file = "report_Ozone.html", output_dir = getwd())Drop V9 because it is missing more than 40% of its values.
Ozone2 <- Ozone2 %>% select(-v9)
vis_miss(Ozone2)
gg_miss_var(Ozone2)
gg_miss_var(Ozone2, show_pct = TRUE)
Ozone2 <- drop_na(Ozone2)
vis_miss(Ozone2)
gg_miss_var(Ozone2)
gg_miss_var(Ozone2, show_pct = TRUE)
Boruta.Ozone <- Boruta(v4 ~ ., data = Ozone2, doTrace = 2, ntree = 500) 1. run of importance source... 2. run of importance source... 3. run of importance source... 4. run of importance source... 5. run of importance source... 6. run of importance source... 7. run of importance source... 8. run of importance source... 9. run of importance source... 10. run of importance source... 11. run of importance source...After 11 iterations, +1.9 secs: confirmed 8 attributes: v1, v10, v11, v12, v13 and 3 more; rejected 1 attribute: v3; still have 2 attributes left. 12. run of importance source... 13. run of importance source... 14. run of importance source... 15. run of importance source... 16. run of importance source... 17. run of importance source... 18. run of importance source... 19. run of importance source... 20. run of importance source... 21. run of importance source... 22. run of importance source... 23. run of importance source... 24. run of importance source... 25. run of importance source... 26. run of importance source... 27. run of importance source... 28. run of importance source... 29. run of importance source... 30. run of importance source... 31. run of importance source... 32. run of importance source... 33. run of importance source... 34. run of importance source... 35. run of importance source... 36. run of importance source... 37. run of importance source... 38. run of importance source... 39. run of importance source... 40. run of importance source... 41. run of importance source... 42. run of importance source... 43. run of importance source... 44. run of importance source... 45. run of importance source... 46. run of importance source... 47. run of importance source... 48. run of importance source... 49. run of importance source... 50. run of importance source... 51. run of importance source... 52. run of importance source... 53. run of importance source... 54. run of importance source... 55. run of importance source... 56. run of importance source... 57. run of importance source...After 57 iterations, +7 secs: rejected 1 attribute: v6; still have 1 attribute left. 58. run of importance source... 59. run of importance source... 60. run of importance source... 61. run of importance source... 62. run of importance source... 63. run of importance source... 64. run of importance source... 65. run of importance source... 66. run of importance source... 67. run of importance source... 68. run of importance source... 69. run of importance source... 70. run of importance source... 71. run of importance source... 72. run of importance source... 73. run of importance source... 74. run of importance source... 75. run of importance source... 76. run of importance source... 77. run of importance source... 78. run of importance source... 79. run of importance source... 80. run of importance source... 81. run of importance source... 82. run of importance source... 83. run of importance source... 84. run of importance source... 85. run of importance source... 86. run of importance source... 87. run of importance source... 88. run of importance source... 89. run of importance source... 90. run of importance source... 91. run of importance source... 92. run of importance source... 93. run of importance source... 94. run of importance source... 95. run of importance source... 96. run of importance source... 97. run of importance source... 98. run of importance source... 99. run of importance source...Boruta.OzoneBoruta performed 99 iterations in 11.35731 secs.
8 attributes confirmed important: v1, v10, v11, v12, v13 and 3 more;
2 attributes confirmed unimportant: v3, v6;
1 tentative attributes left: v2;plot(Boruta.Ozone)
Boruta.Short <- Boruta(v4 ~ ., data = Ozone2, maxRuns = 12)Boruta.Short Boruta performed 11 iterations in 1.280683 secs.
8 attributes confirmed important: v1, v10, v11, v12, v13 and 3 more;
No attributes deemed unimportant.
3 tentative attributes left: v2, v3, v6;plot(Boruta.Short)
TentativeRoughFix(Boruta.Short)Boruta performed 11 iterations in 1.280683 secs.
Tentatives roughfixed over the last 11 iterations.
8 attributes confirmed important: v1, v10, v11, v12, v13 and 3 more;
3 attributes confirmed unimportant: v2, v3, v6;getConfirmedFormula(Boruta.Ozone)v4 ~ v1 + v5 + v7 + v8 + v10 + v11 + v12 + v13
<environment: 0x55f1f6a41740>attStats(Boruta.Ozone) meanImp medianImp minImp maxImp normHits decision
v1 11.38549593 11.3331640 9.68566046 13.162742 1.0000000 Confirmed
v2 1.55894218 1.6259764 -0.85756928 4.042441 0.3939394 Tentative
v3 0.09186004 -0.1900958 -2.46503158 1.533308 0.0000000 Rejected
v5 12.59161861 12.5488172 11.20744361 14.213741 1.0000000 Confirmed
v6 1.77246692 1.7865771 0.00650445 4.109143 0.1616162 Rejected
v7 15.16972281 15.0820582 13.12507543 17.046716 1.0000000 Confirmed
v8 23.89470197 23.9360266 21.83980517 25.746238 1.0000000 Confirmed
v10 16.70222620 16.5953093 14.94116814 18.942722 1.0000000 Confirmed
v11 16.21452670 16.2515251 14.15761384 18.133767 1.0000000 Confirmed
v12 22.16119872 22.0825431 20.51171911 25.151638 1.0000000 Confirmed
v13 10.35425625 10.4030250 7.91369805 12.125914 1.0000000 ConfirmedplotImpHistory(Boruta.Ozone)
Question 2
What are the important variables identified by the Boruta algorithm from the titanic training data? Provide code below to run the Boruta algorithm on the titanic_train data.
Answer:
Code:
library(titanic)
data(titanic_train)Question 3:
- Define clear what the metric kappa measures.
Answer:
- Use an AI tool you are comfortable with, Google AI Studio, ChatGPT, Mistral, Microsoft Colab, or another to prepare a response to the prompt provided in part a). Comment on the similarity of your answer with that provided by an AI tool.
Answer:
Question 4:
Using your selected AI tool, upload or share the steps01.docx with the AI.
- Write a prompt to summarize and explain the ideas presented in the document. Ask for further details be added to the summary for each step. Ask for example R and Python code using the tidyverse and tidymodels package.
Answer: prompt
Answer: summary of the AI generated summary of the steps01.docx.