R: Apply the model

catboost.predict {catboost} | R Documentation

## Apply the model

### Description

Apply the model to the given dataset.

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.

### 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://catboost.ai/docs/concepts/output-data.html). Possible values: 'Probability' 'LogProbability' 'Class' 'RawFormulaVal' 'Exponent' 'RMSEWithUncertainty' 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

### Value

Vector of predictions (matrix for multi-class classification).

### See Also

https://catboost.ai/docs/concepts/r-reference_catboost-predict.html

[Package catboost version 0.26.1 Index]