lease I need help in R Data mining program: I have a clinical dataset (1400 cases) and I applied 4

Aug 15th, 2016
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lease I need help in R Data mining program: I have a clinical dataset (1400 cases) and I applied 4 data mining techniques (ANN, Decision Tree, SVM, Logistic Regression) to predict the binary outcome (Yes, No). Now, I want to improve prediction accuracy through ensemble methods. What are the criteria to choose which model can be combined with another model? And how can that be done in R?

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Answer-----------------------Yes this can be done in R.You can use the caret package to compare models, but for automatically build anensemble you can use the package caretensemble. Read the vignettes first!Creating ensembles is as much art as it is science if you want to do it manually. But itgives you more control over what is happening. It all depends a bit on which kind ofassembling you want to do.Voting ensembles: combine the outcome of multiple predictions and have a majority vote. I.e.If you have the predictions of 3 models, and 2 models predict a 1 and 1 model a 0, theoutcome is 1.Averaging: Average the outcome of multiple models by taking the mean.With both voting and averaging, less highly correlated models work better than highlycorrelated models. But even highly correlated models might improve the final answer, so itbears checking out.There are more methods, but starting with these is a good way to see what is going on.Now, I want to improve prediction accuracy through ensemble methods.What are the criteria to choose which model can be combined with another model? Andhow can that be done in R-------------------------------------------------------------------------Creating ensembles from submission filesThe most basic and convenient way to ensemble is to ensemble Kaggle submission CSVfiles. You only need the predictions on the test set for these methods no need to retrain amodel. This makes it a quick way to ensemble already existing model pr

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