Available in: GBM, DRF, Deep Learning, AutoML, XGBoost, Isolation Forest
This option specifies the tolerance value by which a model must improve before training ceases. For example, given the following options:
then the moving average for last 4 stopping rounds is calculated (the first moving average is reference value for other 3 moving averages to compare).
The model will stop if the ratio between the best moving average and reference moving average is more or equal 1-1e-3 (the misclassification is the less the better metric, for the more the better metrics the ratio have to be less or equal 1+1e-3 to stop).
These stopping options are used to increase performance by restricting the number of models that get built.
stopping_roundsmust be enabled for
For all supported algorithms except AutoML and Isolation Forest, this value defaults to 0.001. In AutoML, this value defaults to 0.001 if the dataset is at least 1 million rows; otherwise it defaults to a bigger value determined by the size of the dataset and the non-NA-rate. In Isolation Forest, this value defaults to 0.01.