GitHub - ucheynna/ML-cancer-remission: Developed regularization and tree-based machine learning models to predict remission status in a cohort of 5059 patients. Elastic net and Random Forest models were compared on F1 scores accuracy, sensitivity, specificity, and AUC ROC.

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Developed regularization and tree-based machine learning models to predict remission status in a cohort of 5059 patients. Elastic net and Random Forest models were compared on F1 scores accuracy, sensitivity, specificity, and AUC ROC.

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491923_README.txt		491923_README.txt
ML_cancer_remission.Rmd		ML_cancer_remission.Rmd
README.txt		README.txt
assignment2024.csv		assignment2024.csv

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README
Glad you've come this far!!
Please note the following
1. Two important documents have been provided. A a markdown script and a csv file
2. Please do not move the ".csv" file from folder while trying to run "ML_cancer_remission.Rmd"
3. To run "ML_cancer_remission", open and make sure the wroking directory is set by clicking on “Set Working Directory”>>>Then click on “To Source File Location”
4. Run script, "install.packages" have been commented out, install as prompted/needed

Note: R code was written on R version 4.3.2 (2023-10-31 ucrt) -- "Eye Holes"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
Recent or latest versions of R and R studio are compatible.