https://github.com/compcode1/depression-screening-ml
The goal of this project was to build a machine learning model capable of accurately predicting depression in a population where the incidence rate is 15%.
https://github.com/compcode1/depression-screening-ml
auc datapreprocessing f1-score hyperparameter-tuning threshold xgboost-classifier
Last synced: 7 months ago
JSON representation
The goal of this project was to build a machine learning model capable of accurately predicting depression in a population where the incidence rate is 15%.
- Host: GitHub
- URL: https://github.com/compcode1/depression-screening-ml
- Owner: Compcode1
- License: gpl-3.0
- Created: 2024-10-22T00:01:30.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-10-22T00:10:26.000Z (12 months ago)
- Last Synced: 2024-10-22T21:32:25.241Z (12 months ago)
- Topics: auc, datapreprocessing, f1-score, hyperparameter-tuning, threshold, xgboost-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 173 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
The goal of this project was to build a machine learning model capable of accurately predicting depression in a population where the incidence rate is 15%. The challenge was to prioritize recall—ensuring that the model captures as many true positive cases as possible—while also maintaining reasonable precision to limit false positives, given budgetary constraints. Using the XGBoost algorithm, we employed a structured approach that included data preparation, hyperparameter tuning, and threshold classification manipulation to optimize model performance. The techniques applied, particularly hyperparameter tuning and adjusting the classification threshold, were highly effective, ultimately leading to a substantial improvement in the F1 score and meeting our project goals.