--- metadata: - name: generator content: Diplodoc Platform v5.52.0 alternate: - https://catboost.ai/docs/en/concepts/python-reference_catboost_modelcompare.md - href: en/concepts/python-reference_catboost_modelcompare.md type: text/markdown title: Markdown version - href: ../llms.txt type: text/markdown title: llms.txt --- > **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt # compare Draw train and evaluation metrics in [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook.md) for two trained models. ## Method call format {#compare__method-call-format} ```python 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) ``` ## Parameters {#parameters} ### model #### Description The CatBoost model to compare with. **Possible types** CatBoost Model **Default value** Required parameter ### metrics #### Description The list of metrics to be calculated. [Supported metrics](https://catboost.ai/docs/en/references/custom-metric__supported-metrics.md) For example, if the AUC and Logloss metrics should be calculated, use the following construction: ```python ['Logloss', 'AUC'] ``` **Possible types** list of strings **Default value** Required parameter ### data #### Description A file or matrix with the input dataset, on which the compared metric values should be calculated. **Possible types** catboost.Pool **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, 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. 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 types** 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, 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. 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 types** 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, 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. 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 metrics are calculated for the following tree ranges: `[0, 2)`, `[0, 4)`, ... , `[0, N)` **Possible types** int **Default value** 1 (the trees are applied sequentially: the first tree, then the first two trees, etc.) ### thread_count #### Description The number of threads to use. Optimizes the speed of execution. This parameter doesn't affect results. **Possible types** int **Default value** -1 (the number of threads is equal to the number of processor cores) ### tmp_dir #### Description The name of the temporary directory for intermediate results. **Possible types** String **Default value** None (the name is generated) ### 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 ## Examples {#examples} ```python from catboost import Pool, CatBoost 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 = CatBoost(params={'iterations': 100, 'learning_rate': 0.1}) model1.fit(train_dataset, verbose=False) model2 = CatBoost(params={'iterations': 100, 'learning_rate': 0.3}) model2.fit(train_dataset, eval_set=eval_dataset, verbose=False) model1.compare(model2, eval_dataset, ['RMSE']) ``` The following is a chart plotted with [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook.md) for the given example. ![](../images/interface__visualization-tools__jupyter__comparemodel__catboost.png)