returnResamp: A character string indicating how much of the resampled summary metrics should be saved. But for teaching purposes it would be very nice to have a caret implementation. This is probably the reason that the validation set approach is not one of caret's preset methods. returnData: A logical for saving the data. In practice, one likes to use k-fold Cross validation, or Leave-one-out cross validation, as they make better use of the data. verboseIter: A logical for printing a training log. 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 data with two classes, there are specialized functions for measuring model performance. For repeated k-fold cross-validation only: the number of complete sets of folds to compute. 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.
#Timeslice cross validation caret how to
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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