compare

{% include model-compare-compare__purpose %}

{{ dl--invoke-format }} {#compare__method-call-format}

compare(model,
        data=None,
        metrics=None,
        ntree_start=0,
        ntree_end=0,
        eval_period=1,
        thread_count=-1,
        tmp_dir=None,
        log_cout=sys.stdout,
        log_cerr=sys.stderr)

{{ dl--parameters }} {#parameters}

model

Description

The {{ product }} model to compare with.

Possible types

{{ product }} Model

Default value

{{ python--required }}

metrics

Description

The list of metrics to be calculated. Supported metrics For example, if the {{ error-function--AUC }} and {{ error-function--Logit }} metrics should be calculated, use the following construction:

['Logloss', 'AUC']

Possible types

{{ python-type--list-of-strings }}

Default value

{{ python--required }}

data

Description

A file or matrix with the input dataset, on which the compared metric values should be calculated.

Possible types

{{ python-type--pool }}

Default value

{{ python--required }}

ntree_start

Description

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 eval_period parameter to k to calculate metrics on every k-th iteration.

{% include eval-start-end-ntree_start__short-param-desc %}

Possible types

{{ python-type--int }}

Default value

{{ fit--ntree_start }}

ntree_end

Description

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 eval_period parameter to k to calculate metrics on every k-th iteration.

{% include eval-start-end-ntree_end__short-param-desc %}

Possible types

{{ python-type--int }}

Default value

{{ fit--ntree_end }}

eval_period

Description

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 eval_period parameter to k to calculate metrics on every k-th iteration.

{% include eval-start-end-python_r__eval__period__desc__no-example %}

In this case, the metrics are calculated for the following tree ranges: [0, 2), [0, 4), ... , [0, N)

Possible types

{{ python-type--int }}

Default value

{{ fit--staged-predict-eval-period }}

thread_count

Description

The number of threads to use.

{% include reusage-thread_count__cpu_cores__optimizes-the-speed-of-execution %}

Possible types

{{ python-type--int }}

Default value

{{ fit__thread_count__wrappers }}

tmp_dir

Description

The name of the temporary directory for intermediate results.

Possible types

{{ data-type__String }}

Default value

None (the name is generated)

{% include python__log-params %}

{{ dl--example }} {#examples}

from catboost import Pool, CatBoostRegressor

train_data = [[0, 3],
              [4, 1],
              [8, 1],
              [9, 1]]
train_labels = [0, 0, 1, 1]

eval_data = [[1, 3],
             [4, 2],
             [8, 2],
             [8, 3]]

eval_labels = [1, 0, 0, 1]

train_dataset = Pool(train_data, train_labels)

eval_dataset = Pool(eval_data, eval_labels)

model1 = CatBoostRegressor(iterations=100, learning_rate=0.1)
model1.fit(train_dataset, verbose=False)

model2 = CatBoostRegressor(iterations=100, learning_rate=0.3)
model2.fit(train_dataset, verbose=False)

model1.compare(model2, eval_dataset, ['RMSE'])

{% include reusage-code-examples-graph-plotted-with-jupyter-notebook %}