Machine Learning - Lab 1 Independent Assignment
[Enter your name]
September 12, 2023
The lab provides space to work with data and to reflect on how the concepts and techniques introduced in each lab might apply to your own research.
To earn a badge for each lab, you are required to respond to a set of prompts for two parts:
In Part I, you will extend our model by adding another variable.
In Part II, you will reflect on your understanding of key concepts and begin to think about potential next steps for your own study.
Part I: Extending our model
In this part of the badge activity, please add another variable – a variable for the number of days before the start of the module students registered. This variable will be a third predictor. By adding it, you’ll be able to examine how much more accurate your model is (if at al, as this variable might not have great predictive power). Note that this variable is a number and so no pre-processing is necessary.
In doing so, please move all of your code needed to run the analysis over from your case study file here. This is essential for your analysis to be reproducible. You may wish to break your code into multiple chunks based on the overall purpose of the code in the chunk (e.g., loading packages and data, wrangling data, and each of the machine learning steps).
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidymodels)## ── Attaching packages ────────────────────────────────────── tidymodels 1.1.1 ──## ✔ broom 1.0.5 ✔ rsample 1.2.0
## ✔ dials 1.2.0 ✔ tune 1.1.2
## ✔ infer 1.0.5 ✔ workflows 1.1.3
## ✔ modeldata 1.2.0 ✔ workflowsets 1.0.1
## ✔ parsnip 1.1.1 ✔ yardstick 1.2.0
## ✔ recipes 1.0.8## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ scales::discard() masks purrr::discard()
## ✖ dplyr::filter() masks stats::filter()
## ✖ recipes::fixed() masks stringr::fixed()
## ✖ dplyr::lag() masks stats::lag()
## ✖ yardstick::spec() masks readr::spec()
## ✖ recipes::step() masks stats::step()
## • Use suppressPackageStartupMessages() to eliminate package startup messageslibrary(janitor)##
## Attaching package: 'janitor'## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(readr)
students <- read_csv("C:\\Users\\srava\\OneDrive\\Documents\\machine-learning\\lab-1\\data\\oulad-students.csv")## Rows: 32593 Columns: 15## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): code_module, code_presentation, gender, region, highest_education, ...
## dbl (6): id_student, num_of_prev_attempts, studied_credits, module_presentat...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.glimpse(students)## Rows: 32,593
## Columns: 15
## $ code_module <chr> "AAA", "AAA", "AAA", "AAA", "AAA", "AAA", "…
## $ code_presentation <chr> "2013J", "2013J", "2013J", "2013J", "2013J"…
## $ id_student <dbl> 11391, 28400, 30268, 31604, 32885, 38053, 4…
## $ gender <chr> "M", "F", "F", "F", "F", "M", "M", "F", "F"…
## $ region <chr> "East Anglian Region", "Scotland", "North W…
## $ highest_education <chr> "HE Qualification", "HE Qualification", "A …
## $ imd_band <chr> "90-100%", "20-30%", "30-40%", "50-60%", "5…
## $ age_band <chr> "55<=", "35-55", "35-55", "35-55", "0-35", …
## $ num_of_prev_attempts <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ studied_credits <dbl> 240, 60, 60, 60, 60, 60, 60, 120, 90, 60, 6…
## $ disability <chr> "N", "N", "Y", "N", "N", "N", "N", "N", "N"…
## $ final_result <chr> "Pass", "Pass", "Withdrawn", "Pass", "Pass"…
## $ module_presentation_length <dbl> 268, 268, 268, 268, 268, 268, 268, 268, 268…
## $ date_registration <dbl> -159, -53, -92, -52, -176, -110, -67, -29, …
## $ date_unregistration <dbl> NA, NA, 12, NA, NA, NA, NA, NA, NA, NA, NA,…students <- students %>%
mutate(pass = ifelse(final_result == "Pass", 1, 0)) %>% # creates a new variable named "pass" and a dummy code of 1 if value of final_result equals "pass" and 0 if not
mutate(pass = as.factor(pass)) # makes the variable a factor, helping later stepsstudents <- students %>%
mutate(disability = as.factor(disability))
view(students)
students %>%
count(id_student) # this many students## # A tibble: 28,785 × 2
## id_student n
## <dbl> <int>
## 1 3733 1
## 2 6516 1
## 3 8462 2
## 4 11391 1
## 5 23629 1
## 6 23632 1
## 7 23698 1
## 8 23798 1
## 9 24186 1
## 10 24213 2
## # ℹ 28,775 more rowsstudents %>%
count(code_module, code_presentation) # this many offerings## # A tibble: 22 × 3
## code_module code_presentation n
## <chr> <chr> <int>
## 1 AAA 2013J 383
## 2 AAA 2014J 365
## 3 BBB 2013B 1767
## 4 BBB 2013J 2237
## 5 BBB 2014B 1613
## 6 BBB 2014J 2292
## 7 CCC 2014B 1936
## 8 CCC 2014J 2498
## 9 DDD 2013B 1303
## 10 DDD 2013J 1938
## # ℹ 12 more rowsstudents <- students %>%
mutate(multiple_depravity = factor(imd_band, levels = c("0-10%",
"10-20%",
"20-30%",
"30-40%",
"40-50%",
"50-60%",
"60-70%",
"70-80%",
"80-90%",
"90-100%"))) %>% # this creates a factor with ordered levels
mutate(multiple_depravity = as.integer(multiple_depravity)) # this changes the levels into integers based on the order of the factor levels
students## # A tibble: 32,593 × 17
## code_module code_presentation id_student gender region highest_education
## <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 AAA 2013J 11391 M East Angli… HE Qualification
## 2 AAA 2013J 28400 F Scotland HE Qualification
## 3 AAA 2013J 30268 F North West… A Level or Equiv…
## 4 AAA 2013J 31604 F South East… A Level or Equiv…
## 5 AAA 2013J 32885 F West Midla… Lower Than A Lev…
## 6 AAA 2013J 38053 M Wales A Level or Equiv…
## 7 AAA 2013J 45462 M Scotland HE Qualification
## 8 AAA 2013J 45642 F North West… A Level or Equiv…
## 9 AAA 2013J 52130 F East Angli… A Level or Equiv…
## 10 AAA 2013J 53025 M North Regi… Post Graduate Qu…
## # ℹ 32,583 more rows
## # ℹ 11 more variables: imd_band <chr>, age_band <chr>,
## # num_of_prev_attempts <dbl>, studied_credits <dbl>, disability <fct>,
## # final_result <chr>, module_presentation_length <dbl>,
## # date_registration <dbl>, date_unregistration <dbl>, pass <fct>,
## # multiple_depravity <int>set.seed(20230709)
train_test_split <- initial_split(students, prop = .80)
data_train <- training(train_test_split)
data_test <- testing(train_test_split)
data_train## # A tibble: 26,074 × 17
## code_module code_presentation id_student gender region highest_education
## <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 EEE 2014J 648834 M Scotland HE Qualification
## 2 DDD 2013J 572089 M Scotland Lower Than A Lev…
## 3 BBB 2013J 335136 F Scotland HE Qualification
## 4 EEE 2014J 636124 M East Midla… Post Graduate Qu…
## 5 DDD 2014J 637691 M East Midla… A Level or Equiv…
## 6 FFF 2013J 188524 F West Midla… Lower Than A Lev…
## 7 FFF 2013B 2560595 M East Angli… Lower Than A Lev…
## 8 CCC 2014J 634776 M East Midla… A Level or Equiv…
## 9 FFF 2014B 618397 M East Angli… HE Qualification
## 10 GGG 2014J 698019 F Wales Lower Than A Lev…
## # ℹ 26,064 more rows
## # ℹ 11 more variables: imd_band <chr>, age_band <chr>,
## # num_of_prev_attempts <dbl>, studied_credits <dbl>, disability <fct>,
## # final_result <chr>, module_presentation_length <dbl>,
## # date_registration <dbl>, date_unregistration <dbl>, pass <fct>,
## # multiple_depravity <int>data_test## # A tibble: 6,519 × 17
## code_module code_presentation id_student gender region highest_education
## <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 AAA 2013J 28400 F Scotland HE Qualification
## 2 AAA 2013J 31604 F South East… A Level or Equiv…
## 3 AAA 2013J 45642 F North West… A Level or Equiv…
## 4 AAA 2013J 62155 F North West… HE Qualification
## 5 AAA 2013J 110175 M East Angli… HE Qualification
## 6 AAA 2013J 114999 M Yorkshire … HE Qualification
## 7 AAA 2013J 116541 M Wales HE Qualification
## 8 AAA 2013J 127582 F East Midla… A Level or Equiv…
## 9 AAA 2013J 141355 F East Midla… A Level or Equiv…
## 10 AAA 2013J 145130 M South West… HE Qualification
## # ℹ 6,509 more rows
## # ℹ 11 more variables: imd_band <chr>, age_band <chr>,
## # num_of_prev_attempts <dbl>, studied_credits <dbl>, disability <fct>,
## # final_result <chr>, module_presentation_length <dbl>,
## # date_registration <dbl>, date_unregistration <dbl>, pass <fct>,
## # multiple_depravity <int>my_rec <- recipe(pass ~ disability + imd_band + date_registration, data = data_train)
my_rec## ## ── Recipe ──────────────────────────────────────────────────────────────────────## ## ── Inputs## Number of variables by role## outcome: 1
## predictor: 3my_mod <-
logistic_reg()
my_mod <-
logistic_reg() %>%
set_engine("glm") %>% # generalized linear model
set_mode("classification") # since we are predicting a dichotomous outcome, specify classification; for a number, specify regression
my_mod## Logistic Regression Model Specification (classification)
##
## Computational engine: glmmy_wf <-
workflow() %>% # create a workflow
add_model(my_mod) %>% # add the model we wrote above
add_recipe(my_rec) # add our recipe we wrote above
fitted_model <- fit(my_wf, data = data_train)
fitted_model## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: logistic_reg()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 0 Recipe Steps
##
## ── Model ───────────────────────────────────────────────────────────────────────
##
## Call: stats::glm(formula = ..y ~ ., family = stats::binomial, data = data)
##
## Coefficients:
## (Intercept) disabilityY imd_band10-20 imd_band20-30%
## -0.65487 -0.29256 0.11654 0.12186
## imd_band30-40% imd_band40-50% imd_band50-60% imd_band60-70%
## 0.31315 0.29855 0.39549 0.45231
## imd_band70-80% imd_band80-90% imd_band90-100% date_registration
## 0.39705 0.50083 0.54349 0.00169
##
## Degrees of Freedom: 25147 Total (i.e. Null); 25136 Residual
## (926 observations deleted due to missingness)
## Null Deviance: 33350
## Residual Deviance: 33080 AIC: 33100final_fit <- last_fit(my_wf, split = train_test_split)
final_fit## # Resampling results
## # Manual resampling
## # A tibble: 1 × 6
## splits id .metrics .notes .predictions .workflow
## <list> <chr> <list> <list> <list> <list>
## 1 <split [26074/6519]> train/test spl… <tibble> <tibble> <tibble> <workflow>final_fit %>%
collect_predictions()## # A tibble: 6,519 × 7
## id .pred_0 .pred_1 .row .pred_class pass .config
## <chr> <dbl> <dbl> <int> <fct> <fct> <chr>
## 1 train/test split 0.651 0.349 2 0 1 Preprocessor1_Model1
## 2 train/test split 0.586 0.414 4 0 1 Preprocessor1_Model1
## 3 train/test split 0.540 0.460 8 0 1 Preprocessor1_Model1
## 4 train/test split 0.592 0.408 14 0 1 Preprocessor1_Model1
## 5 train/test split 0.584 0.416 32 0 1 Preprocessor1_Model1
## 6 train/test split 0.579 0.421 36 0 1 Preprocessor1_Model1
## 7 train/test split 0.683 0.317 37 0 1 Preprocessor1_Model1
## 8 train/test split 0.601 0.399 41 0 1 Preprocessor1_Model1
## 9 train/test split 0.609 0.391 48 0 1 Preprocessor1_Model1
## 10 train/test split 0.565 0.435 52 0 1 Preprocessor1_Model1
## # ℹ 6,509 more rowsfinal_fit %>%
collect_predictions() %>% # see test set predictions
select(.pred_class, pass) %>% # just to make the output easier to view
mutate(correct = .pred_class == pass) # create a new variable, correct, telling us when the model was and was not correct## # A tibble: 6,519 × 3
## .pred_class pass correct
## <fct> <fct> <lgl>
## 1 0 1 FALSE
## 2 0 1 FALSE
## 3 0 1 FALSE
## 4 0 1 FALSE
## 5 0 1 FALSE
## 6 0 1 FALSE
## 7 0 1 FALSE
## 8 0 1 FALSE
## 9 0 1 FALSE
## 10 0 1 FALSE
## # ℹ 6,509 more rowsfinal_fit %>%
collect_predictions() %>% # see test set predictions
select(.pred_class, pass) %>% # just to make the output easier to view
mutate(correct = .pred_class == pass) %>% # create a new variable, correct, telling us when the model was and was not correct
tabyl(correct)## correct n percent valid_percent
## FALSE 2325 0.35664979 0.3696931
## TRUE 3964 0.60806872 0.6303069
## NA 230 0.03528148 NAstudents %>%
count(pass)## # A tibble: 2 × 2
## pass n
## <fct> <int>
## 1 0 20232
## 2 1 12361students %>%
mutate(prediction = sample(c(0, 1), nrow(students), replace = TRUE)) %>%
mutate(correct = if_else(prediction == 1 & pass == 1 |
prediction == 0 & pass == 0, 1, 0)) %>%
tabyl(correct)## correct n percent
## 0 16281 0.4995244
## 1 16312 0.5004756Previous results:0.6308522
New results: 0.6303069
How does the accuracy of this new model compare? Add a few reflections below:
Part II: Reflect and Plan
Part A: Please refer back to Breiman’s (2001) article for these three questions.
- Can you summarize the primary difference between the two cultures of statistical modeling that Breiman outlines in his paper?
Building models that accurately reflect the underlying processes that produce the data is the focus of the data modeling culture. This approach frequently makes use of data distribution hypotheses and parametric statistical models.The development of predictive models that may produce accurate forecasts without necessarily divulging the underlying data generation method is the core emphasis of the algorithmic modeling culture.
- How has the advent of big data and machine learning affected or reinforced Breiman’s argument since the article was published?
Increased Relevance of Algorithmic Modeling: With the growth of big data, there is an increasing demand for predictive models that can handle enormous and complex data efficiently. Handling complexity: Deep learning and ensemble methods, for instance, have demonstrated their ability to identify intricate patterns in data even when the underlying data generation process is unclear. Applications in the Real World: Many practical applications, like fraud detection, natural language processing, and recommendation systems, depend on the accuracy of predictions.
- Breiman emphasized the importance of predictive accuracy over understanding why a method works. To what extent do you agree or disagree with this stance?
The individual conditions and objectives usually impact the viewpoint on this subject: Breiman and I concur that In many real-world scenarios, especially those involving complex data and high-dimensional landscapes, prediction accuracy is the primary objective. Understanding the underlying mechanics may not be crucial if a model is capable of making reliable predictions and performs its intended purpose well. Breiman and I disagree, however, as it is still crucial to understand why a method works in other contexts, especially in scientific research and industries where interpretability is essential (such as healthcare). Making wiser judgments and gaining new insights may be aided by knowing the processes and causal relationships.
Part B:
- How good was the machine learning model you developed in the badge activity? What if you read about someone using such a model as a reviewer of research? Please add your thoughts and reflections following the bullet point below.
The machine learning model developed for the badge activity appears to function modestly, and the context in which it is employed affects how well it works. Although there is potential for improvement through other modeling approaches and adjustments, it could be useful in some situations.
- How might the model be improved? Share any ideas you have at this time below:
We may look at include more variables and experimenting with different combinations to see which ones provide the best results in order to increase the model’s accuracy. The performance of the model’s prediction can be enhanced by investigating feature engineering techniques that create new variables from preexisting ones.
Part C: Use the institutional library (e.g. NU Library), Google Scholar or search engine to locate a research article, presentation, or resource that applies machine learning to an educational context aligned with your research interests. More specifically, locate a machine learning study that involves making predictions.
Provide an APA citation for your selected study. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, New York, 2011). GOOGLE SCHOLAR 2 K. Murphy, Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, MA, 2012).
What research questions were the authors of this study trying to address and why did they consider these questions important? The authors aim to address the following broad educational objectives rather than specific research questions:
Comprehensive Coverage: To provide a comprehensive and in-depth understanding of machine learning and statistical learning, covering various algorithms, methods, and techniques.
Foundational Knowledge: To equip readers with the foundational knowledge required to work in the field of machine learning, making them well-versed in the theories, principles, and mathematics behind these techniques.
Applications: To explore practical applications of machine learning and statistical learning in various domains, demonstrating how these techniques can be used to solve real-world problems. -
- What were the results of these analyses? A thorough review of machine learning and statistical learning principles, techniques, and approaches may be found in the textbooks “The Elements of Statistical Learning” by Hastie, Tibshirani, and Friedman and “Machine Learning: A Probabilistic Perspective” by Kevin Murphy. These books are teaching tools rather than research studies, hence they do not contain particular research analyses or experimental findings. These books don’t convey the results of research experiments or analyses in the same manner as research articles or studies do, but they are nonetheless intended to give readers a strong theoretical and practical understanding of the subject matter.
Please feel free to ask any particular questions you may have concerning the machine learning ideas, methods, or algorithms discussed in these textbooks, and I’ll try my best to answer them or to give information based on the material in the books.
Regenerate
- Knit and Publish
Complete the following steps to knit and publish your work:
First, change the name of the
author:in the YAML header at the very top of this document to your name. The YAML header controls the style and feel for knitted document but doesn’t actually display in the final output.Next, click the knit button in the toolbar above to “knit” your R Markdown document to a HTML file that will be saved in your R Project folder. You should see a formatted webpage appear in your Viewer tab in the lower right pan or in a new browser window. Let your instructor know if you run into any issues with knitting.
Finally, publish your webpage on Rpubs by clicking the “Publish” button located in the Viewer Pane after you knit your document.
Your First Machine Learning Badge
Congratulations, you’ve completed your first badge activity! To receive credit for this assignment and earn your first official Lab Badge, submit the link on Blackboard and share with your instructor.
Once your instructor has checked your link, you will be provided a physical version of the badge below!