R: Apply the model with several virtual ensembles

catboost.virtual_ensembles_predict {catboost} | R Documentation

## Apply the model with several virtual ensembles

### Description

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'

### Usage

    catboost.virtual_ensembles_predict(
      model,
      pool,
      verbose = FALSE,
      prediction_type = "VirtEnsembles",
      ntree_end = 0L,
      virtual_ensembles_count = 10,
      thread_count = -1
    )

### Arguments

`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: 'VirtEnsembles' 'TotalUncertainty' 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

### Value

Matrix or Array of predictions (for 'TotalUncertainty' and 'VirtEnsembles' prediction_type correspondingly)

### See Also

https://catboost.ai/docs/concepts/python-reference_virtual_ensembles_predict.html?lang=en

[Package catboost version 1.0.3 Index]