# catboost.staged_predict

```
catboost.staged_predict(model,
pool,
verbose = FALSE,
prediction_type = "RawFormulaVal",
ntree_start = 0,
ntree_end = 0,
eval_period = 1,
thread_count = -1)
```

## Purpose

Apply the model to the given dataset and calculate the results for the specified trees only.

## Arguments

Argument | Description | Default value |
---|---|---|

model | The model obtained as the result of training. | Required argument |

pool | The input dataset. | Required argument |

verbose | Verbose output to stdout. | FALSE (not used) |

prediction_type | The required prediction type. Supported prediction types: - Probability
- Class
- RawFormulaVal
| RawFormulaVal |

ntree_start | To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to This parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based. | 0 |

ntree_end | To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to This parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based. | 0 (the index of the last tree to use equals to the number of trees in the model minus one) |

eval_period | To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to This parameter defines the step to iterate over the range - ntree_start is set 0
- ntree_end is set to N (the total tree count)
- eval_period is set to 2
In this case, the results are returned for the following tree ranges: | 1 (the trees are applied sequentially: the first tree, then the first two trees, etc.) |

thread_count | The number of threads to use during the training. Optimizes the speed of execution. This parameter doesn't affect results. | -1 (the number of threads is equal to the number of processor cores) |

Argument | Description | Default value |
---|---|---|

model | The model obtained as the result of training. | Required argument |

pool | The input dataset. | Required argument |

verbose | Verbose output to stdout. | FALSE (not used) |

prediction_type | The required prediction type. Supported prediction types: - Probability
- Class
- RawFormulaVal
| RawFormulaVal |

ntree_start |
This parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based. | 0 |

ntree_end |
This parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based. | 0 (the index of the last tree to use equals to the number of trees in the model minus one) |

eval_period |
This parameter defines the step to iterate over the range - ntree_start is set 0
- ntree_end is set to N (the total tree count)
- eval_period is set to 2
In this case, the results are returned for the following tree ranges: | 1 (the trees are applied sequentially: the first tree, then the first two trees, etc.) |

thread_count | The number of threads to use during the training. Optimizes the speed of execution. This parameter doesn't affect results. | -1 (the number of threads is equal to the number of processor cores) |