R: Apply the model

catboost.predict {catboost} | R Documentation

## Apply the model

### Description

Apply the model to the given dataset.

### Usage

    catboost.predict(model, pool, verbose = FALSE,
      prediction_type = "RawFormulaVal", ntree_start = 0, ntree_end = 0,
      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://tech.yandex.com/catboost/doc/dg/concepts/output-data-docpage/#output-data). Possible values: 'Probability' 'Class' 'RawFormulaVal' Default value: 'RawFormulaVal'
`ntree_start` | Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). Default value: 0
`ntree_end` | Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). Default value: 0 (if value equals to 0 this parameter is ignored and ntree_end equal to tree_count)
`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

### Details

Peculiarities: In case of multiclassification the prediction is returned in the form of a matrix. Each line of this matrix contains the predictions for one object of the input dataset.

### See Also

https://tech.yandex.com/catboost/doc/dg/concepts/r-reference_catboost-predict-docpage/

[Package catboost version 0.16.1 Index]