R: Calculate metrics.

catboost.eval_metrics {catboost} | R Documentation

## Calculate metrics.

### Description

Calculate the specified metrics for the specified dataset.

### Usage

    catboost.eval_metrics(
      model,
      pool,
      metrics,
      ntree_start = 0L,
      ntree_end = 0L,
      eval_period = 1,
      thread_count = -1,
      tmp_dir = NULL
    )

### Arguments

`model` | The model obtained as the result of training. Default value: Required argument
`pool` | The pool for which you want to evaluate the metrics. Default value: Required argument
`metrics` | The list of metrics to be calculated. (Supported metrics https://catboost.ai/docs/references/custom-metric__supported-metrics.html) Default value: Required argument
`ntree_start` | Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). Default value: 0
`ntree_end` | Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). Default value: 0 (if value equals to 0 this parameter is ignored and ntree_end equal to tree_count)
`eval_period` | Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). Default value: 1
`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
`tmp_dir` | The name of the temporary directory for intermediate results. If NULL, then the name will be generated. Default value: NULL

### Value

dict: metric -> array of shape [(ntree_end - ntree_start) / eval_period].

### See Also

https://catboost.ai/docs/concepts/python-reference_catboost_eval-metrics.html

[Package catboost version 0.26 Index]