Machine Learning - Lab 1 Independent Assignment
Austin James
April 2, 2024
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 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ 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 1.2.0 ──
## ✔ broom 1.0.5 ✔ rsample 1.2.1
## ✔ dials 1.2.1 ✔ tune 1.2.0
## ✔ infer 1.0.7 ✔ workflows 1.1.4
## ✔ modeldata 1.3.0 ✔ workflowsets 1.1.0
## ✔ parsnip 1.2.1 ✔ yardstick 1.3.1
## ✔ recipes 1.0.10
## ── 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()
## • Dig deeper into tidy modeling with R at https://www.tmwr.orglibrary(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.teststudents <- read_csv("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.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))
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 levels
set.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, data = data_train)
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_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)
final_fit <- last_fit(my_wf, train_test_split)
students %>%
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#new code for the 3rd variable
library(tidyverse)
library(tidymodels)
library(janitor)
students <- read_csv("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.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))
#new third variable is created here
students <- students %>%
mutate(date_registration_new = as.factor(date_registration))
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 levels
set.seed(20230712)
train_test_split <- initial_split(students, prop = .80)
data_train <- training(train_test_split)
data_test <- testing(train_test_split)
#my new variable was added here
my_rec <- recipe(pass ~ disability + imd_band + date_registration_new, data = data_train)
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_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)
final_fit <- last_fit(my_wf, train_test_split)## → A | error: factor date_registration_new has new levels -289, -281, -276, -241, -189, 27, 32, 37, 40, 44, 81
## There were issues with some computations A: x1There were issues with some computations A: x1## Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
## information.students %>%
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 0.4990335Previous results: correct 0 n 16328 – .5009665% ….. correct 1 n 16265 -.499035%
New results: correct 0 n 16328 – .5009665% ….. correct 1 n 16265 -.499035%
How does the accuracy of this new model compare? Add a few reflections below: There was no change to the model after I included the new variable
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 is when you can already assume the function while the algorithmic culture is when it is unknown and have to find the function.
- How has the advent of big data and machine learning affected or reinforced Breiman’s argument since the article was published?
- The bigger the data it is harder to understand the algorithm so algorithmic culture is more common in big data because we will not be able to easly identify how a model should be formed
- Breiman emphasized the importance of predictive accuracy over understanding why a method works. To what extent do you agree or disagree with this stance?
- I agree that the importance of predicicive accuracy is more important than understanding why it works. In the end of the day the CEO of a company will care only about the end results and not how the team got to the answer.
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.
- I think the model is a good start but it can be expanded on. I think 50% is ok but it can be improved on. I would question the model if it only was accurate 50% of the time.
- How might the model be improved? Share any ideas you have at this time below:
- I think the model can be improved on if we are able to include the variables that take into account the education level of the student before taking the class.
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.
- Nieto, Y., Diaz, V., Montenegro, C., Gonzalez, C., & Crespo, R. (2019, May 27). Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions. https://ieeexplore.ieee.org/abstract/document/8723336
What research questions were the authors of this study trying to address and why did they consider these questions important?
- The team was trying to figure out if they could create a model that was able to predict if a engineering student from University of Columbia would graduate
What were the results of these analyses?
- They were able to create a model that was 83% accurate. I would find that very succesful for a model.
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!