# 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,
log_cout=sys.stdout,
log_cerr=sys.stderr)


## Parameters

### data

#### Description

A file or matrix with the input dataset.

Possible values

catboost.Pool

Default value

Required parameter

### metrics

#### Description

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']


Possible values

list of strings

Default value

Required parameter

### ntree_start

#### Description

To reduce the number of trees to use when the model is applied or the metrics are calculated, setthe 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.

Possible values

int

Default value

0

### ntree_end

#### Description

To reduce the number of trees to use when the model is applied or the metrics are calculated, setthe range of the tree indices to[ntree_start; ntree_end) and the step of the trees to use toeval_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.

Possible values

int

Default value

0 (the index of the last tree to use equals to the number of trees in the
model minus one)

### eval_period

#### Description

To reduce the number of trees to use when the model is applied or the metrics are calculated, setthe range of the tree indices to[ntree_start; ntree_end) and the step of the trees to use toeval_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).

Possible values

int

Default value

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

#### Description

The number of threads to use during the training.

Optimizes the speed of execution. This parameter doesn't affect results.

Possible values

int

Default value

-1 (the number of threads is equal to the number of processor cores)

### log_cout

Output stream or callback for logging.

Possible types

• callable Python object
• python object providing the write() method

Default value

sys.stdout

### log_cerr

Error stream or callback for logging.

Possible types

• callable Python object
• python object providing the write() method

Default value

sys.stderr

## Type of return value

A dictionary of calculated metrics in the following format:

metric -> array of shape [(ntree_end – ntree_start) / eval_period]