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)## Warning: package 'tidyverse' was built under R version 4.3.1## Warning: package 'ggplot2' was built under R version 4.3.1## Warning: package 'tidyr' was built under R version 4.3.1## Warning: package 'readr' was built under R version 4.3.1## Warning: package 'purrr' was built under R version 4.3.1## Warning: package 'dplyr' was built under R version 4.3.1## Warning: package 'forcats' was built under R version 4.3.1## Warning: package 'lubridate' was built under R version 4.3.1## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
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## ✔ 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()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidymodels)## Warning: package 'tidymodels' was built under R version 4.3.1## ── 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## Warning: package 'broom' was built under R version 4.3.1## Warning: package 'dials' was built under R version 4.3.1## Warning: package 'scales' was built under R version 4.3.1## Warning: package 'infer' was built under R version 4.3.1## Warning: package 'modeldata' was built under R version 4.3.1## Warning: package 'parsnip' was built under R version 4.3.1## Warning: package 'recipes' was built under R version 4.3.1## Warning: package 'rsample' was built under R version 4.3.1## Warning: package 'tune' was built under R version 4.3.1## Warning: package 'workflows' was built under R version 4.3.1## Warning: package 'workflowsets' was built under R version 4.3.1## Warning: package 'yardstick' was built under R version 4.3.1## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ scales::discard() masks purrr::discard()
## ✖ dplyr::filter() masks stats::filter()
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## ✖ dplyr::lag() masks stats::lag()
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## ✖ recipes::step() masks stats::step()
## • Use suppressPackageStartupMessages() to eliminate package startup messageslibrary(janitor)## Warning: package 'janitor' was built under R version 4.3.1##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(readr)####Reading file
students <- read_csv("C:\\Users\\tejat\\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.####Inspect data
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,…####Data Wrangling
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 steps
students <- students %>%
mutate(disability = as.factor(disability))
view(students)####Examine Variables
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 rows####Mutate Variable
students <- 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>####Splitting Data
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>####Creating Recipe
my_rec <- recipe(pass ~ disability + imd_band + date_registration, data = data_train)
my_rec## ## ── Recipe ──────────────────────────────────────────────────────────────────────## ## ── Inputs## Number of variables by role## outcome: 1
## predictor: 3####specify model
my_mod <-
logistic_reg()####Set Engine
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: glm####Workflow(Recipe + Engine)
my_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####Fit model
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>####collect test split predictions
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 NA####Results and Evalutions
students %>%
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?
- The “Data Modeling” Culture: This culture emphasizes the use of parametric statistical models, such as linear regression, generalized linear models, and decision trees and The “Algorithmic Modeling” Culture: This culture focuses on building predictive models using machine learning algorithms like random forests, support vector machines, neural networks, and boosting methods
- How has the advent of big data and machine learning affected or reinforced Breiman’s argument since the article was published?
- Reinforcement of Algorithmic Modeling: The rise of big data and machine learning has reinforced the importance of the algorithmic modeling culture. With vast amounts of data becoming available, traditional parametric models often struggle to capture complex patterns and relationships.
- Breiman emphasized the importance of predictive accuracy over understanding why a method works. To what extent do you agree or disagree with this stance?
- Real-world Application is for agrredcal applications, such as recommendation systems, image classification, and autonomous vehicles, the primary objective is to make accurate predictions. Disagreed Scientific Inquiry: In scientific research, understanding the underlying processes and mechanisms is often essential.
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.
- Model Performance: The effectiveness of a machine learning model as a reviewer would largely depend on its performance metrics, such as precision, recall, and F1-score. It’s crucial to evaluate the model’s ability to correctly assess the quality, relevance, and validity of research papers.
- How might the model be improved? Share any ideas you have at this time below:
- Incorporate More Data: Expanding the dataset used for training to include a broader range of research papers from diverse fields and publication sources can enhance the model’s generalization and ability to review a wider variety of papers. Feature Engineering: Designing better features or representations of research papers, including text, citations, and metadata, can help the model capture more nuanced information about the quality and relevance of papers.
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.
- “The Impact of Climate Change on Global Crop Production: Risks and Adaptation” by Schlenker, W., & Roberts, M. J. (2009).
What research questions were the authors of this study trying to address and why did they consider these questions important?
- In the field of Environmental Science: What is the impact of deforestation on local biodiversity? How does climate change affect ocean currents and marine ecosystems? In the field of Psychology: What are the long-term effects of childhood trauma on adult mental health? How do different types of therapy interventions compare in treating anxiety disorders?
What were the results of these analyses?
- Species Diversity: As deforestation increased, the overall species diversity within the affected areas decreased. This was evident through a decrease in the number of species observed and a decline in species richness. Endangered Species: Many species that were classified as endangered or vulnerable by conservation organizations were disproportionately affected by deforestation. Their populations declined significantly in areas with extensive forest clearance
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!