| catboost.virtual_ensembles_predict {catboost} | R Documentation |
Apply the model to the given dataset using several independent truncated models - virtual ensembles. Each tree in ensemble predicts its own value for each document from pool.
Peculiarities: Return value varies on prediction_type: array for 'VirtEnsembles' and matrix for 'TotalUncertainty'
catboost.virtual_ensembles_predict( model, pool, verbose = FALSE, prediction_type = "VirtEnsembles", ntree_end = 0L, virtual_ensembles_count = 10, thread_count = -1 )
model |
The model obtained as the result of training. Default value: Required argument |
pool |
The input dataset. Default value: Required argument |
verbose |
Verbose output to stdout. Default value: FALSE (not used) |
prediction_type |
The format for displaying approximated values in output data (see https://catboost.ai/docs/concepts/python-reference_virtual_ensembles_predict.html#python-reference_catboostclassifier_predict__output-format). Possible values:
Default value: 'VirtEnsembles' |
ntree_end |
Index of the first tree not to be used when applying the model or calculating the metrics (zero-based indexing). Default value: 0 (the index of the last tree to use equals to the number of trees in the model minus one) |
virtual_ensembles_count |
Number of tree ensembles to use. Each virtual ensemble can be considered as truncated model. Default value: 10 |
thread_count |
The number of threads to use when applying the model. If -1, then the number of threads is set to the number of CPU cores. Allows you to optimize the speed of execution. This parameter doesn't affect results. Default value: -1 |
Matrix or Array of predictions (for 'TotalUncertainty' and 'VirtEnsembles' prediction_type correspondingly)
https://catboost.ai/docs/concepts/python-reference_virtual_ensembles_predict.html?lang=en