plotLearningCurves: Utility function to plot learning curves of multiple models.
In andybeeching/discern: A Collection of Utilities for Data Analyse

- Comparison helps determine if models contain bias or variance - Ideally want low training error and good generalization - Basic algorithm: split original data at different ratios - Re-train passed-in models on each split, and capture the error rate for both the train and valid data-sets. - NOTE: CV error is evaluated on _entire_ CV set, not subset - Plot the error (the score) versus the dataset size

1	plotLearningCurves(models, labels, metric, ctrlFn, cv, colors, seed = 1)

`models`	List - List of model objects.
`labels`	List - Optional List of labels associated with models
`metric`	String - Metric models were trained with
`cv`	Data - Cross-validation data set (containing predictor)
`colors`	List - Optional List of colors associated with models
`seed`	Number - Seed to use for training each model

plotLearningCurves(
  models     = list(cfFit),
  metric     = "ROC",
  ctrlFn     = fitControl,
  cv         = valid,
  colors     = c("orange", "blue"),
  labels     = c("Glm Model 4", "Glm Model 6", "CForest"),
  seed       = SEED
)