Which is better, RFE or rfeiter in caret?
To run the resamples in parallel, the code for rfe does not change; prior to the call to rfe, a parallel backend is registered with foreach (see the examples below). rfeIter is the basic algorithm while rfe wraps these operations inside of resampling. To avoid selection bias, it is better to use the function rfe than rfeIter .
What is the default value for the RFE function?
By default, possible values are “RMSE” and “Rsquared” for regression and “Accuracy” and “Kappa” for classification. If custom performance metrics are used (via the functions argument in rfeControl, the value of metric should match one of the arguments.
How is rdocumentation used in the RFE function?
This function implements backwards selection of predictors based on predictor importance ranking. The predictors are ranked and the less important ones are sequentially eliminated prior to modeling. The goal is to find a subset of predictors that can be used to produce an accurate model.
What kind of processor is used for RFE?
By default, rfe will use a single processor on the host machine. As of version 4.99 of this package, the framework used for parallel processing uses the foreach package. To run the resamples in parallel, the code for rfe does not change; prior to the call to rfe, a parallel backend is registered with foreach (see the examples below).
When to use recursive feature elimination in caret?
The web page http://topepo.github.io/caret/recursive-feature-elimination.html#rfe has more details and examples related to this function. rfe can be used with “explicit parallelism”, where different resamples (e.g. cross-validation group) can be split up and run on multiple machines or processors.
What happens when resamples are not saved in RFE?
When updating a model, if the entire set of resamples were not saved using rfeControl (returnResamp = “final”), the existing resamples are removed with a warning. a list of size length (sizes) + 1 containing the column names of the “surviving” predictors at each stage of selection.
Which is an important option when using RFE?
There are two important configuration options when using RFE: the choice in the number of features to select and the choice of the algorithm used to help choose features. Both of these hyperparameters can be explored, although the performance of the method is not strongly dependent on these hyperparameters being configured well.
How is RFE used in feature selection algorithms?
RFE is a wrapper-type feature selection algorithm. This means that a different machine learning algorithm is given and used in the core of the method, is wrapped by RFE, and used to help select features. This is in contrast to filter-based feature selections that score each feature and select those features with the largest (or smallest) score.