To use a Hausarbeit schreiben lassen service to ensure the quality of your work when writing on ROC curve analysis. This Python script is a program that performs receiver operating characteristic (ROC) curve analysis on data stored in an Excel file. It first imports data from an Excel file using the pandas library and then calculates the area under the curve (AUC) value using the scikit-learn library. It also calculates the true positive rate (TPR) and false positive rate (FPR) using the scikit-learn library. The program then plots the ROC curve using matplotlib and saves the plot as a .png file. Finally, it creates a dataframe using pandas and exports it to an Excel file.
How to Run the Code
To run the following Python script:
- Make sure you have the necessary modules imported at the top of the script:
sklearn,pandas,matplotlib. If you don’t have these modules installed, you will need to install them usingpip install [module]. - Modify the file path for the input data file in the
pathvariable to the location of your input data file. - If desired, modify the sheet name and file path for the output Excel file in the
sheetandoutput_pathvariables, respectively. - Run the script. The ROC curve will be plotted and the AUC value, TPR, and FPR values will be calculated and outputted to an Excel file. The ROC curve will also be saved as a .png file.
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Overall Code
References:
- Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, É. Scikit-Learn: Machine Learning in Python. Journal of Machine Learning Research 2011, 12 (85), 2825–2830.
- Hunter, J. D. Matplotlib: A 2D Graphics Environment. Comput Sci Eng 2007, 9 (3), 90–95. https://doi.org/10.1109/MCSE.2007.55.
- Reback, J.; McKinney, W.; jbrockmendel; Bossche, J. van den; Augspurger, T.; Cloud, P.; gfyoung; Sinhrks; Klein, A.; Roeschke, M.; Hawkins, S.; Tratner, J.; She, C.; Ayd, W.; Petersen, T.; Garcia, M.; Schendel, J.; Hayden, A.; MomIsBestFriend; Jancauskas, V.; Battiston, P.; Seabold, S.; chris-b1; h-vetinari; Hoyer, S.; Overmeire, W.; alimcmaster1; Dong, K.; Whelan, C.; Mehyar, M. Pandas-Dev/Pandas: Pandas 1.0.3. March 18, 2020. https://doi.org/10.5281/ZENODO.3715232.
- van Rossum, G. Python Reference Manual; Centrum voor Wiskunde en Informatica (CWI), 1995.
Classifies, quantifies, and explores data using spectroscopy techniques coupled with chemometric routines.
