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 1.1.2 ✔ 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.teststudents <- read.csv("oulad-students.csv")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 <int> 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 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ studied_credits <int> 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 <int> 268, 268, 268, 268, 268, 268, 268, 268, 268…
## $ date_registration <int> -159, -53, -92, -52, -176, -110, -67, -29, …
## $ date_unregistration <int> 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))students <- students %>%
mutate(imd_band = 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(imd_band = as.integer(imd_band)) # this changes the levels into integers based on the order of the factor levelsset.seed(20230712)
train_test_split <- initial_split(students, prop = .80)
data_train <- training(train_test_split)
data_test <- testing(train_test_split)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()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 abovefitted_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_band date_registration
## -0.667029 -0.280013 0.059134 0.001643
##
## Degrees of Freedom: 22371 Total (i.e. Null); 22368 Residual
## (3702 observations deleted due to missingness)
## Null Deviance: 29800
## Residual Deviance: 29580 AIC: 29590final_fit <- (last_fit(fitted_model, 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 0.640 0.360 2 0 1 Preprocessor1_Model1
## 2 train/test split 0.598 0.402 4 0 1 Preprocessor1_Model1
## 3 train/test split 0.632 0.368 7 0 1 Preprocessor1_Model1
## 4 train/test split NA NA 10 <NA> 1 Preprocessor1_Model1
## 5 train/test split 0.620 0.380 16 0 0 Preprocessor1_Model1
## 6 train/test split NA NA 18 <NA> 1 Preprocessor1_Model1
## 7 train/test split 0.617 0.383 21 0 1 Preprocessor1_Model1
## 8 train/test split 0.591 0.409 24 0 1 Preprocessor1_Model1
## 9 train/test split 0.537 0.463 33 0 1 Preprocessor1_Model1
## 10 train/test split NA NA 35 <NA> 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 <NA> 1 NA
## 5 0 0 TRUE
## 6 <NA> 1 NA
## 7 0 1 FALSE
## 8 0 1 FALSE
## 9 0 1 FALSE
## 10 <NA> 1 NA
## # 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 2071 0.3176868 0.3728844
## TRUE 3483 0.5342844 0.6271156
## NA 965 0.1480288 NAstudents %>%
count(pass)## pass n
## 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 16328 0.5009665
## 1 16265 0.4990335```
Previous results:
New results:
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?
- Data Modeling (Culture 1):
Focuses on modeling data’s probability distribution,Uses linear regression and traditional stats,Aims to understand variable correlations,Algorithmic
Modeling (Culture 2):
Prioritizes predictive accuracy, Utilizes methods like random forests,Seeks precise predictions, less emphasis on understanding data generation.
- How has the advent of big data and machine learning affected or reinforced Breiman’s argument since the article was published?
- Data volume , complexity, predictions are effected or reinforced breiman’s argument.
- Breiman emphasized the importance of predictive accuracy over understanding why a method works. To what extent do you agree or disagree with this stance?
Many agree in practical fields like machine learning, accurate predictions are high valued.
many disagree on scientific research, considering context and interpretability trade-offs.
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 in the badge activity demonstrated strong predictive ability,valuable for research assessment with consideration for interpretability and responsible use.
- How might the model be improved? Share any ideas you have at this time below:
-The model could potentially be enhanced through feature engineering to capture more relevant predictors or by experimenting with different algorithms and hyperparameters for improved performance.
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.
- Sanusi, I. T., Oyelere, S. S., & Omidiora, J. O. (2022). Exploring teachers’ preconceptions of teaching machine learning in high school: A preliminary insight from Africa. Computers and Education Open, 3, 100072. https://doi.org/10.1016/j.caeo.2021.100072
What research questions were the authors of this study trying to address and why did they consider these questions important?
- This paper delved into the preconceptions that 12 high school teachers in Africa had regarding the teaching of machine learning. The study identified and analyzed five distinct categories of these preconceptions and examined their interrelationships.it is because important for improving teacher training, curriculum development, student learning outcomes, and the overall effectiveness of integrating machine learning into the educational system. It can lead to more relevant and engaging educational experiences that better prepare students for the demands of the modern workforce.
What were the results of these analyses?
- this study result provides insight into how the involved teachers viewed the sense of introducing machine learning into high schools and what to be fulfilled to teach the concept effectively.
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!