returnResamp: A character string indicating how much of the resampled summary metrics should be saved. returnData: A logical for saving the data. Warning message: 'missing values in resampled performance measures' in caret train() using rpart Not definitively sure without more data. verboseIter: A logical for printing a training log. The sensitivity and specificity are computed for each cutoff and the ROC curve is computed. For two class problems, a series of cutoffs is applied to the predictor data to predict the class. I understand that one could use statistical methods such as ARIMA, etc, but I was wondering if there were similar methods in ML that can be applied here, ** in particular ** using packages in R (such as randomForest, caret, party, etc). For repeated k-fold cross-validation only: the number of complete sets of folds to compute. For classification, ROC curve analysis is conducted on each predictor. The task is to predict the MonthEndExam score of the current month using the student's historical data on performance during previous months.įor the current month, the scores on all the individual subjects are known by mid-month, whereas the MonthEndExam is not known until the end of the month and it is what we would like to predict. The same data is collected for the students in Month 2, 3. Using an example, suppose we wanted to find based on monthly scores on subjects for each student how well he/she will do in a month-end exam that occurs every month. I am trying to determine how to use machine learning models such as for eg., random Forest with (non-financial) time-series data.
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