Description Usage Arguments Examples
- 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 |
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