caret (version 6.0-71)

learing_curve_dat: Create Data to Plot a Learning Curve


For a given model, this function fits several versions on different sizes of the total training set and returns the results


learing_curve_dat(dat, outcome = NULL, proportion = (1:10)/10, test_prop = 0, verbose = TRUE, ...)


the training data
a character string identifying the outcome column name
the incremental proportions of the training set that are used to fit the model
an optional proportion of the data to be used to measure performance.
a logical to print logs to the screen as models are fit
options to pass to train to specify the model. These should not include x, y, formula, or data.


a data frame with columns for each performance metric calculated by train as well as columns: as well as columns:In the results, each data set size will have one row for the apparent error rate, one row for the test set results (if used) and as many rows as resamples (e.g. 10 rows if 10-fold CV is used).


This function creates a data set that can be used to plot how well the model performs over different sized versions of the training set. For each data set size, the performance metrics are determined and saved. If test_prop == 0, the apparent measure of performance (i.e. re-predicting the training set) and the resampled estimate of performance are available. Otherwise, the test set results are also added.

If the model being fit has tuning parameters, the results are based on the optimal settings determined by train.

See Also



## Not run: 
# set.seed(1412)
# class_dat <- twoClassSim(1000)
# set.seed(29510)
# lda_data <- learing_curve_dat(dat = class_dat, 
#                               outcome = "Class",
#                               test_prop = 1/4, 
#                               ## `train` arguments:
#                               method = "lda", 
#                               metric = "ROC",
#                               trControl = trainControl(classProbs = TRUE, 
#                                                        summaryFunction = twoClassSummary))
# ggplot(lda_data, aes(x = Training_Size, y = ROC, color = Data)) + 
#   geom_smooth(method = loess, span = .8) + 
#   theme_bw()
#  ## End(Not run)