Please upload your input file:

OR



After uploading your file, there are a few parameter and configurations that need to be set.
Hover onto the title for descriptions of each parameter. If you are not sure about some parameters, leave them default.


Classifier: In M2ROC, we provide three options for classifiers.

Support Vector Machine
Naive Bayes
Random Forest


Number of Estimators This parameter is used if you chose Random Forest as your classifier. It represents the number of decision trees built by the random forest classifier. In general, higher number of estimators give a better performance. *Although, increasing this number significantly slows the proccessing.



Number of Folds: This parameter is used for cross validation.Number of folds represent the number of equal size subsamples partitioned from the input data. Of the n subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k-1 subsamples are used as training data. The cross-validation process is then repeated k times (the folds), with each of the k subsamples used exactly once as the validation data. *Notice that the number of folds in cross validation should be between 2 and the number of samples inclusive. If your data has a small sample size, consider using LeaveOneOut cross validation for best performance.



Plot Lengend Size: This cosmetic parameter determines the size of the legend in the plots.



Plot Line Width: This cosmetic parameter determines the line width of curves in the plots.



Legend Title: This cosmetic parameter determines the title of legend in the plots.