#Caret models manual

The function bwplot.resamples is used to generate this plot and if you look at the underlying code, the variables are factorized based on their average performance under the metric of interest.īelow I have the relevant code that does the factorization: bwplot.resamples <- function (x, data = NULL, models = x$models, metric = x$metric. Model = factor(model, levels = c('MARS', 'CART', 'CondInfTree'))) %>% To change the order change the levels of model (this approach also works with ggplot2): resamps$values %>% Scale_y_discrete(limits = c('MARS', 'CART', 'CondInfTree')) To set a specific order on the y axis: p1 + Geom_boxplot(aes(x = RMSE, y = model)) -> p1 #and plot the box plot Mutate(model = sub('~RMSE', '', model)) %>% #leave just the model names Gather(model, RMSE, -1) %>% #convert to long table Select(1, ends_with('RMSE')) %>% #select the first column and all columns with a name ending with 'RMSE' Since you did not post an example with data I will use one of the examples from ?caret:::bwplot.resamples library(caret) This way you will have much better control over the final plot.

I recommend plotting the boxplots manually using ggplot2. Perhaps there is one but my lattice skills are lacking. them through cross-validation, and evaluating the trained models. train can be used with explicit parallelism. regression model with caret The caret package is designed for selecting models. Downloadable The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich.

#Caret models manual

I am not aware of an easy solution using bwplot method from caret package. The vignette entitled caret Manual - Model Building has more details and examples related to this function.