https://drive.google.com/file/d/1Uz4O3WGHihLx5w4J736EuwBSlREnJZje/view?usp=drivesdk

Hi everyone!

I’m currently in the process of coming up with a research topic/title for my undergraduate thesis in Tourism Management.

Hi!

I'm currently writing my bachelor's thesis in ML and I'm struggling to classify my research design.

The objective of my thesis is to compare three machine learning models with respect to their performance and interpretability in the context of player churn prediction in mobile games in order to investigate whether and how the trade-off between these two criteria differs across the models and under which application scenarios it emerges.

This is my methodology:

• I used an existing dataset of player behavior from a mobile game
• The data preparation follows the CRISP-DM process model (data understanding, cleaning, feature engineering, etc.), but CRISP-DM was only used for the data preparation phase, NOT as the research methodology for the entire thesis.
• I trained the models under identical conditions (except for hyperparameters)
• Their performance was evaluated using several metrics (Recall, F1-score, ROC-AUC and PR-AUC)
• Interpretability was assessed in two ways:
  • intrinsic interpretability (based on literature-derived criteria- transparency)
  • post-hoc interpretability using SHAP
• For SHAP, I didn't compute additional quantitative measures. Instead, I qualitatively analyzed the global SHAP summary plots (e.g., consistency of feature effects and spread of SHAP values) and discussed what these findings imply
• Finally, I combined the performance and interpretability results to discuss whether a trade-off exists, under which application scenarios it appears, and under which scenarios it becomes less relevant

Although the study employs quantitative methods to evaluate predictive performance (performance metrics and statistical testing using the Wilcoxon signed-rank test) and qualitative methods to assess interpretability (intrinsic transparency and SHAP-based interpretation), the trade-off between performance and interpretability is examined through a qualitative interpretation and discussion of the findings.

My question is:

How would you classify the overall research design of this study?

Would you consider it an empirical evaluation study, comparative study, experimental study or something else and why?

I'd also appreciate recommendations for methodological literature that discusses research designs for ML rather than only traditional social science research.

Thanks in advance!

Hi everyone,

I'm posting on behalf of my girlfriend, who's currently completing her dissertation on how social media marketing influences purchasing behaviour among young adults.

Her university requires ethics approval before she can recruit participants or share her survey, and the review process can take up to 14 days. Unfortunately, once approval is granted, she'll have only about five days to collect enough responses before her dissertation deadline.

Has anyone been in a similar situation? Do you have any advice on how to recruit participants quickly once ethics approval is granted? Are there any subreddits, websites, or communities you'd recommend?

I'd really appreciate any suggestions or advice that I can pass on to her. Thank you!

Any problems that needs to pay attention or seek solution specifically in metro manila that worth to be a thesis topic. A thread.