eval_metrics

Calculate the specified metrics for the specified dataset.

Method call format

eval_metrics(data,
             metrics,
             ntree_start=0,
             ntree_end=0,
             eval_period=1,
             thread_count=-1)

Parameters

Parameter Possible values Description Default value
data catboost.Pool

A file or matrix with the input dataset.

Required parameter
metrics list of strings

The list of metrics to be calculated.

Supported metrics

For example, if the AUC and Logloss metrics should be calculated, use the following construction:

['Logloss', 'AUC']
Required parameter
ntree_start int

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 [ntree_start; ntree_end).

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 int

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 [ntree_start; ntree_end) and the step of the trees to use to eval_period.

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 int

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 [ntree_start; ntree_end) and the step of the trees to use to eval_period.

This parameter defines the step to iterate over the range [ntree_start; ntree_end). For example, let's assume that the following parameter values are set:

  • 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: [0, 2), [0, 4), ... , [0, N).

1 (the trees are applied sequentially: the first tree, then the first two trees, etc.)
thread_count int

The number of threads to use during 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)
Parameter Possible values Description Default value
data catboost.Pool