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 'tibble' 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 'stringr' 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 1.1.2 ✔ 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)## 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()
## ✖ 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)## 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.teststudents <- read_csv("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 stepsstudents <- students %>%
mutate(disability = as.factor(disability))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(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
students## # A tibble: 32,593 × 16
## 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 10 more variables: imd_band <int>, 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>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 + 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)## # 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 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:
-The accuracy of the new model is 62% -frist I installed all the required packages later point of time I imported csv file to the current directory to it. Later i created and run all the code and did knit in it and uploaded in Rpubs
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 primary difference between these two cultures lies in their fundamental philosophies and methodologies. The data modeling culture seeks to understand and describe data using explicit probabilistic models, while the algorithmic modeling culture prioritizes predictive accuracy through data-driven, often complex algorithms. Breiman’s paper discusses the merits and limitations of each culture and highlights the need for a balanced approach that leverages the strengths of both in practical data analysis and modeling.
- 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 Culture:
Big Data: The explosion of big data has made it increasingly challenging to fit traditional data models to vast and complex datasets. Algorithmic modeling approaches, which are often more flexible and can handle high-dimensional data, have gained prominence. Machine Learning: Machine learning algorithms, particularly deep learning, have demonstrated remarkable success in handling large-scale data and complex patterns. This reinforces the algorithmic modeling culture’s focus on predictive accuracy. Increased Emphasis on Prediction:
Big Data: With the availability of massive datasets, organizations often prioritize predictive modeling over a deep understanding of underlying data processes. This aligns with the algorithmic modeling culture’s emphasis on prediction rather than interpretation. Machine Learning: Machine learning models excel in predictive tasks, and their use has grown in fields like recommendation systems, natural language processing, and image recognition, further emphasizing the importance of predictive power.
- Breiman emphasized the importance of predictive accuracy over understanding why a method works. To what extent do you agree or disagree with this stance?
- Agreeing with Emphasis on Predictive Accuracy:
Real-world Impact: In many real-world scenarios, the primary goal of modeling is to make accurate predictions or decisions. For example, in medical diagnosis or autonomous driving, predictive accuracy can be a matter of life and death, and understanding why a model works might be less critical.
Complex Data: In cases involving complex, high-dimensional, or big data, explicit data models may not capture the underlying patterns effectively. In such situations, algorithmic models that prioritize accuracy and adaptability can be more suitable.
Disagreeing with Exclusive Emphasis on Predictive Accuracy:
Interpretability: In some domains, model interpretability is crucial for regulatory compliance, ethics, and user trust. Understanding why a model makes certain predictions can be essential for explaining its decisions to stakeholders.
Bias and Fairness: Algorithms that prioritize predictive accuracy may inadvertently reinforce biases in the data. Understanding the model’s inner workings can help identify and mitigate bias, ensuring fairness and equity.
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 Evaluation: The effectiveness of a machine learning model used for reviewing research would depend on various factors, including the quality and diversity of the training data, the choice of features, and the model’s architecture. Assessing the model’s performance through rigorous evaluation is crucial to ensure its reliability.
Bias and Fairness: Machine learning models can inherit biases present in the data they are trained on. It’s essential to critically evaluate and address any biases that might affect the model’s reviews of research to ensure fairness and objectivity.
Human Oversight: Even with a well-performing model, human oversight is crucial in research review processes. Researchers bring domain expertise, critical thinking, and context that a model might lack. The model should serve as an aid to reviewers rather than a replacement.
- How might the model be improved? Share any ideas you have at this time below:
- Model Evaluation: The effectiveness of a machine learning model used for reviewing research would depend on various factors, including the quality and diversity of the training data, the choice of features, and the model’s architecture. Assessing the model’s performance through rigorous evaluation is crucial to ensure its reliability.
Bias and Fairness: Machine learning models can inherit biases present in the data they are trained on. It’s essential to critically evaluate and address any biases that might affect the model’s reviews of research to ensure fairness and objectivity.
Human Oversight: Even with a well-performing model, human oversight is crucial in research review processes. Researchers bring domain expertise, critical thinking, and context that a model might lack. The model should serve as an aid to reviewers rather than a replacement.
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.
- Cinar, U. K. (2020). Combining Domain Knowledge & Machine Learning. ACM. https://doi.org/10.1145/3369114.3369125
What research questions were the authors of this study trying to address and why did they consider these questions important?
-What are sutiable approaches to identify bussiness stategies? -what is significant on a customer behavior and reduce costs to its clients?
What were the results of these analyses?
- The result of the analysis was house price was estimated by processing the domain info through the machine learning algorithm .
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!